CHAPTER: 5 PREDICTION USING ARTIFICIAL NEURAL NETWORK

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1 CHAPTER: 5 PREDICTION USING ARTIFICIAL NEURAL NETWORK 5.1 LITERATURE SURVEY: Artificial neural networks (ANN) are data-processing systems which are regulated by biological neural system and are widely used to rectify and solve wide variety of worriment in science and engineering.ann is preferred largely over those areas where the conventional modelling method fails. For very specific application a well trained ANN model can be worn as a predictive model.ann finds its utility and has great competency to prognosticate results from the training an experimental data and then validation can be carried out by independent data.ann can raise the performance if new data are available [105]. An ANN model can entertain multiple input variables with one or multiple hidden layers to predict multiple output variables. The input variables can be trained with various neurons in the hidden layer to generate and predict various output variables. Without having prior information of the process relationship, ANN can understand the system that has to be modelled. This ability of ANN favours it greatly over the conventional modelling technique. As compared to conventional simulation programs the presage done by a well-trained ANN is typically much faster. The speculation behind faster prediction is that ANN uses no lengthy iterative calculations to solve differential equations as it all depends upon the selection of a befitting neural network topology in terms of model incisiveness and primitiveness. Adding to ANN s advantage, the input and output variables can be added or removed if required. Various researchers investigated the individuality of IC engine using ANN [109]. Testing an engine under varying operating circumstances and with different types of fuel is both time consuming and expensive. To rectify this problem an alternative method to foresee the performance and exhaust exhalation of an engine can be modelled using ANNs [106]. ANN has been considered by the author an important tool which has helped in designing the solar steam generating plant. It has widely supported in modelling and performance prediction of solar water heating systems.ann helped the author in estimating the heating loads of the building and correctly predicting the energy consumption of a passive solar building. All these ANN models were backed up by multiple hidden-layer architecture. Thus ANN provided an edge over others to correctly predict various parameters [107]. Refrigeration applications banked on ANN for solving the problem of accuracy in heat rate estimations by developing a suitable ANN model which really transfigured the entire area of utilizing heat exchangers in refrigeration applications. It correctly prognosticated the estimated error in the heat rates [108]. The aim of this paper is to PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 73

2 model the steady-state performance of a vapour-compression liquid heat pump with the use of neural networks. The model uses a generalized radial basis function (GRBF) neural network Models are developed for three different refrigerants, namely LPG, R22and R290. Predicted COP values, when LPG orr22 are pre-owned as refrigerant, are usually accurate to within 2 per cent, whereas many predictions for R290deviate more than 10 per cent [109]. This paper presents a model that uses nonfully connected Feed forward Artificial Neural Networks (FANNs) for the presaging of a seawater-refrigerated power plant condenser performance using the heat transfer rate (Q), the heat transfer coefficient (U) and the cleanliness factor (FC). The erratum in the test stage for Q, U and FC were acceptable, being less than 0.5% for Q, around 4% for U and around 2% for FC. The errors in the forecasting stage for U and FC elevated with respect to the test stage [110]. This paper offers to determine coefficient of performance (COP) and total irreversibility (TI) values of vapour-compression refrigeration system with different refrigerants and their mixtures mentioned above using ANN. Using different learning algorithms and training functions the R values was found to be , maximum errors for training and test data are smaller than 2 and 3%, respectively. It is concluded that, ANNs can be used for prediction of COP and TI as an accurate method in the systems [113].This study probed the applicability of artificial neural networks (ANNs) to predict various performance parameters of a cascade vapour compression refrigeration system. The ANN predictions by and large agreed well with the experimental results with correlation coefficients ranging from and mean relative errors were found in the domain of %. The results theorized that the ANN approach can alternatively and reliably be utilized for modelling cascade refrigeration systems [112]. Investigation were carried out to evaluate the performance and emission parameters with the aid of ANN taking into account biodiesels from different feedstock and petroleum diesel fuels in a diesel engine. Creating the network using log-sigmoid transfer function and back propagation learning algorithm resulted in R2 values of 0.99 and mean % errors less than 4.2 for the training data.r2 value of about 0.99 and mean % errors smaller than 5.5 resulted for the test data. The prediction done by ANN thus generated accurate data. [111]. In this research work ANN was used to study the effects of intake valve timings on the engine performance and fuel economy. The input layer comprised of intake-valve timing, engine speed and engine torque, fuel consumption were the two output parameters to be measured. the root mean squared error (RSME),fraction of variance(r2) and mean absolute percentage error (MAPE) were found to be (0.9017%,0.2860%); (0.9920%,0.9299%) and (7.2613%,7.5448%) respectively for torque and fuel consumption as predicted by ANN. With these findings ANN proved its worth in predicting the engine performance of SI engine[114].in this study ANN model generated good results with correlation coefficient (R) values of ,0.999,0.929 and for engine torque, SFC, CO and HC emissions respectively when experiments were performed on diesel engine using waste cooking biodiesel fuel. The value of MSE (Mean square error) was found to be by the ANN model which depicted good correlation between the measured and simulated values [116]. PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 74

3 Yuan Wang and his co-workers analyzed the effect of cetane number on exhaust emissions from engine [115].In a similar research done using ANN modelling, the prediction for the engine performance, exhaust emissions and exhaust gas temperature was observed with correlation coefficients varying from , mean relative errors in the domain of % and very low root mean square errors for a gasoline engine. Thus ANN showed an upper hand when compared to classical modelling techniques [118]. This research study exemplified the utility of Artificial Intelligence (AI) techniques in modelling and predicting the correct performance and control of combustion process. Focus was laid upon as how AI systems can solve a variety of problems in the area of combustion engineering [107]. Najafi et al. [117] Using E0, E5, E10, E15 and E20 experimentally with the assistance of artificial neural network, Power and torque output of the engine was found to be more. To envisage a link between brake power, torque, brake specific fuel consumption, brake thermal efficiency, volumetric efficiency using different gasoline ethanol blends and speeds as input data, an ANN model was developed. ANN approach can be used to precisely predict the SI engine performance as the study revealed. The ANN results were found to be excellent; R values in this model were very close to 1, while a low value of root mean square errors (RMSE) was observed. It was observed that there is a good link between the ANN-predicted results and the experimental data, as revealed after quality exploration. Thus for the effective correlation and simulation of engine parameters ANN proved to be a constructive tool and ANN emerged as an accurate and simple approach in scrutinizing this complex and multivariate problem. Yucesu et al. [92] divided his research work in two different analysis zone: (i) The experimental analysis: A single cylinder, 4-stroke SI engine became the object of research in his first stage using ethanol-gasoline blends in the proportion of 10%, 20%, 40% and 60%. The tests were performed at wide open throttle (WOT) and running the engine at 2000rpm. Test conditions also included the variation in the ignition timings, relative air-fuel ratio (RAFR) and compression ratio. Results revealed on reducing the ignition timings the brake torque of the engine increased at CR of 8:1 and 10:1 for ethanol blended fuel. At 0.9 RAFR for all test fuels maximum torque was obtained for both compression ratios 8:1 and 10:1.Variation in BSFC was observed and the sole dependency of the variation was on the engine torque and the heating value of the used fuel. (ii) The mathematical modelling analysis: ANN was used as a mathematical tool to analyze the parameters of engine performance at WOT for different fuel densities. The results of experimental values were used for mathematical modelling analysis as training and test data. After training, it was found that the value of R2 (absolute fraction of variance) were recorded as and subsequently for the engine torque and specific fuel consumption. Similarly, these values for testing data were and respectively. To be acquainted with the thermodynamic cycle of the engine research work was carried on a quasi dimensional model. To begin with, ethanol addition to gasoline increased the RVP (Reid vapour pressure) of the blended fuels to a maximum value PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 75

4 at 10vol% ethanol addition and then the RVP decreased, signifying an increase in evaporative emissions for ethanol gasoline blended fuels. Efficiency improved owing to the fact that adding ethanol to gasoline enhanced the octane number and gave higher compression ratio. E30 blend increased the research octane number from 95 to 101. The addition of 15vol% ethanol to the gasoline augmented the peak cylinder pressure, reduction in the peak temperature, reduction in ignition delay, reduction in combustion duration, increment in flame front propagation, and increment in the maximum heat release rate by about 8.5%, 5%, 20%, 5.5%, 7.6% and 10.5% respectively at the compression ratio of 8:1. The addition of 30vol% ethanol to the gasoline caused an increase in the engine power, thermal efficiency, and increase in the SFC by 4 %, 3.5 % and 4.3% respectively at a compression ratio of 8: OBJECTIVES OF PREDICTION BY ANN: After close review carried out through exhaustive literature it was found in close proximity that limited work related to validation has been carried out for individual performance parameters and various regulated emissions. Further no material related to exhaustive correlation between all experimental performance parameters and emissions with ANN predicted results were found such as the one that was used in this study. The following are the main objectives of validation being carried out by ANN. The sole intention of this validation work is to examine the performance and pollutant emissions of a four-stroke SI engine, when it is allowed to operate under specific conditions with varying ethanol-gasoline blends ranging from pure gasoline to pure ethanol. The blends so chosen are E0, E10, E20, E40, E60, E80 and E100. Artificial neural network is employed as the measuring tool to analyze and validate the experimental results with the ANN-predicted results. An ANN model is developed to envisage a correlation between all the performance parameters and emission components using different gasolineethanol blends and with varying engine load from no load, 25, 50, 75% and full load as input data, keeping the speed of the engine at a constant value of 2500 rpm. Almost 70% of the total experimental data is selected at random and is used for training purpose, while the 15% is used for validation. In order to invade the results for network generalization the last 15% of the data is utilized 5.3 ARTIFICIAL NEURAL NETWORKS (ANN): ANN is an analytical method for simulating system performance. ANN has three main layers:- 1. Input 2. Hidden PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 76

5 3. Output The input data are from the external world. The Processing elements are called neurons present in the input layer transfer data from the external world to the hidden layer. The weights are the values of connections between cells. The Data which are in the neurons of input and hidden layer and the bias, summation and activation functions are used to process output data. The summation function is a function which calculates the net input of the cell. The activation function provides a match between input and output layers. It basically determines the output of the cell by processing net input to the cell. The sigmoid function is generally used for the transfer function. u i = n j=1 W 1 ij X 1 j + b 1 i Yi = f Ui output (5.1) Neural Network resembles the human brain. It is quite similar to human brain in two aspects:- The knowledge acquired by the network through a learning process. And inter-neuron connection strengths known as Synaptic Weights are used to store the knowledge. Each neuron receives input X1, X2, X Xn attached with a weight Wi which shins the connection strength for a particular input for each connection. Every input is then multiplied by the corresponding weight of the neuron connection. A bias bi can be defined as a type of connection weight with a constant non-zero value added to the summation of inputs and the corresponding weights u, u i = n j=0 W i jxj + bi (5.2) The summation Ui is transferred using a activation or transfer function f(ui) to yield a value called the units activation given as yi = f Ui (5.3) The method relies on experimental data that is used to train the ANN so that it can precisely predict the system performance at other conditions. This technique has found application in situations where the simulation of complex systems is required but limited experimental data is available. ANN is a powerful, nonlinear tool and since many phenomena in industry have non-linear characteristics, ANN has been applied widely. The performance of the ANN-based predictions is evaluated by regression analysis of the network outputs (predicted parameters) and the experimental values. The error identified during the learning process is called the root-mean-squared-error (RMSE) and is defined as follows: RMSE = n i=1 { ei Pi 2 /N} 1/2 (5.4) PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 77

6 The correlation coefficient (R) and mean relative error (MRE) are used for characterizing the network performance. The correlation coefficient can vary between -1 and +1, but R values closer to +1 indicate a stronger positive linear relationship, while R values closer to -1 indicate a stronger negative relationship. The mean relative error, which shows the mean ratio between the error and the experimental values, is defined as MRE = 1 N N i=1 1x ei pi ei X 100% (5.5) MRE provides an indicator on the predictive error relative to the correct value. The lower the MRE shows a better correlation between the predicted and experimental results. The R value provides an alternative indicator between the predicted and experimental data, when an R value closest to 1 represents the most accurate prediction. R = 1 ei pi pi 2 Where N is the total number of data sets, ei is the experimental value and pi is the network predicted value. (5.6) 5.4 MODELLING WITH ANN: An ANN model was developed to predict a correlation between brake power, torque, brake specific fuel consumption, brake thermal efficiency, volumetric efficiency and emission components using different gasoline ethanol blends and speeds as inputs data. The validation and test data sets are each set to 15% of the original data. With these settings, the input vectors and target vectors will be randomly divided into three sets. Training-These are presented to the network during training and the network is adjusted according to its error. Approximately 70% of the total experimental data (299 samples) was selected at random and was used for training purpose. Validation-These are used to measure network generalization and to halt training when generalization stops improving. The 15 %( 64 samples) was used to validate that the network is generalizing and to stop training before over fitting. Testing-These have no effect on training and so provide an independent measure of network performance during and after training. The last 15 %( 64 samples) was used as a completely independent test of network generalization. The standard network that was used for the function fitting is a two layer feed forward network with a sigmoid transfer function in the hidden layer and a linear transfer function in the output layer. There is 1 neural network structure with 2 inputs and 9 outputs; the input layer consisted of 2 neurons which corresponded to engine load and levels of biofuel blends and the output layer had 9 neurons. The number of hidden layers and neurons within each layer can be designed by the complexity of PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 78

7 the problem and data set. The number of hidden neurons was set to 20.The number of hidden neurons can be raised if the training performance is observed to be poor. The activation function was chosen to be sig/ln. The sig symbol in Table 4.1 represents log-sigmoid transfer function which squashes inputs into (0, 1) range. Linear function suited best for output layer which is shown as lin in Table 5.1 Table 5.1. Summary of different networks evaluated to yield the criteria of network performance Activation function Training rule Neurons in hidden layer Training error R sig/ln Trainlm x sig/ln Trainlm x sig/ln Trainlm x sig/ln Trainlm x sig/ln Trainlm x sig/ln Trainlm x sig/ln Trainlm x sig/ln Trainlm x sig/ln Trainlm x sig/ln Trainlm x Therefore, by sig/lin as an example it is meant log-sigmoid transfer function for hidden layer and linear transfer function for the output layer. This arrangement of functions in function approximation problems or modelling is common and yields better results. A training algorithm was selected to train the network. Levenberg- Marquardt (trainlm) was selected for this research study, but for some noisy and small problems Bayesian Regularization (trainbr) can take longer but can provide a better solution. The trainlm algorithm appears to be the fastest method for training moderate-sized feed forward neural networks (up to several hundred weights).for large problems however Scaled Conjugate Gradient (trainscg) is recommended as it uses gradient calculations which are more memory efficient that the Jacobian calculations, the other two algorithm use. There were two inputs and nine output parameters in the experimental tests. The two input variables are engine load in watts and the percentage of ethanol blending with the conventional gasoline fuels. The nine outputs for evaluating engine parameters are indicated in Fig.5.1. PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 79

8 Fig.5.1. The structure of ANN for SI engine with Gasoline Ethanol blended Fuels. The training and testing performance (MSE) was chosen to be the error criterion.the complexity and size of the network was also an important consideration, and therefore smaller ANNs had to be selected. Different training algorithms were tested and Levenberg Marquardt (trainlm) was selected. R values in Table 5.1 represent the correlation coefficient between the outputs and targets. As seen in Table 5.1, R values did not increase when neurons in the hidden layer is more than 20 such as 21, 22, 23 and 24. Therefore, a network with one hidden layer and 20 neurons was selected as the optimum ANN. Simulations were performed using MATLAB (R2014b), neural network toolbox was used for ANN design. The training was carried out by Levenberg-Marquardt (trainlm) until the validation error failed to decrease for one thousand iterations (validation stops).regression plots were noted as regression is used to validate the network performance. The regression plot displayed the network outputs with respect to targets for training, validation and test sets. For a perfect fit, the data should fall along a 45degree line; where the network outputs are equal to the targets. For this problem the fit was reasonably good for all data sets; with R values in each case of The correlation coefficient pertaining to all parameters is displayed which clearly indicates the perfect fit as all data are found to be accumulated near the line as in Fig PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 80

9 Fig Model Flow Chart Fig. 5.3.Correlation Coefficient for network performance, R (Brake power) PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 81

10 Fig.5.4.Correlation Coefficient for network performance, R (BSFC) PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 82

11 Fig. 5.5.Correlation Coefficient for network performance, R (BTE) PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 83

12 Fig.5.6.Correlation Coefficient for network performance, R (Torque) PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 84

13 Fig.5.7. Correlation Coefficient for network performance, R Volumetric efficiency PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 85

14 Fig.5.8. Correlation Coefficient for network performance, R CO PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 86

15 Fig.5.9. Correlation Coefficient for network performance, R (CO2) PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 87

16 Fig Correlation Coefficient for network performance, R HC PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 88

17 Fig.5.11.Correlation Coefficient for network performance, R NOx PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 89

18 Fig.5.12.Correlation Coefficient for network performance, R (Overall parameters) PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 90

19 Fig Correlation Coefficient for network performance, R PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 91

20 Fig.5.14.Mean square error (MSE) against epochs In case of more accurate results, retraining of the network can be carried out. Retraining the network will change the initial weights and biases of the network and may produce an improved network after retraining. The best validation performance was at epoch 74 as indicated in the Fig Error Histogram was viewed to obtain additional verification of the network performance. As in Fig. 5.15, the blue bar in the error Histogram represents training data, the green bars represent validation data, and the red bars represent testing data. The Histogram gave an indication of outliers, which are data points where the fit was significantly worse than the majority of data. In this current research study it was observed that while most error falls between -6 to 8, there is a training point with an error of 8 and validation points with an error of 4. PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 92

21 Fig.5.15.Error Histogram These outliers are also visible on the testing regression plot. The first corresponds to the point with a target of 1000 and the output near It is a good idea to check the outliers to determine whether the data is bad, or if those data points are different than the rest of the data set. If the outliers are valid data points but are unlike the rest of the data, then the network is extrapolating for these points. In that case more data can be collected which looks like the outlier points, and retrain the network once again. If the performance on the training set is good, but the test set performance is significantly worse, which could indicate over fitting, then reducing the number of neurons can improve the performance results. If the training performance is poor, then increasing the number of neurons can help improving the said training performance parameters. The training of the network is accomplished by adjusting the weights and is carried out the rough a large number of training sets and training cycles (epochs). This ANN model is limited to the research engine that was used in this project operating on at wide open throttle conditions (the engine specifications are given in Table 3.2). The training state plots for the model validation data was trained at epoch of 80 and max fail was close to 1000.After reinitializing the weights and reverting weights the following set of curves were obtained as in Fig PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 93

22 Fig.5.16.Training state plot for model validation (Overall Parameters) Fig.5.17.Training state plot for model validation (Brake Power) PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 94

23 Fig.5.18.Training state plot for model validation (BSFC) Fig.5.19.Training state plot for model validation (BTE) PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 95

24 Fig.5.20.Training state plot for model validation (Torque) Fig.5.21.Training state plot for model validation (Volumetric efficiency) PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 96

25 Fig.5.22.Training state plot for model validation (CO) Fig.5.23.Training state plot for model validation (CO2) PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 97

26 Fig.5.24.Training state plot for model validation (HC) Fig.5.25.Training state plot for model validation (NOx) PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 98

27 5.5 RESULT AND DISCUSSION: In this study, the network was decided to consist of one hidden layer with 20 neurons. The criterion R was selected to evaluate the networks to find the optimum solution. The complexity and size of the network was also an important consideration, and therefore smaller ANNs had to be selected. A regression analysis between the network response and the corresponding targets was performed to investigate the network response in more detail. Thus Levenberg-Marquardt (trainlm) was selected. The R-values in Table 5.1 represent the correlation coefficient between the outputs and targets. The R-value didn t increase beyond 20 neurons in the hidden layers. Consequently the network with 20 neurons in the hidden layers would be considered satisfactory. From all the networks trained, few ones could provide the low error condition, from which the simplest network was chosen. The results showed that the training algorithm of Back-Propagation was sufficient for predicting engine torque, brake power and exhaust gas components for different engine speeds, loads and different fuel blends ratios. There is a high correlation between the predicted values by the ANN model and the measured values resulted from experimental tests, which imply that the model succeeded in prediction of the engine performance. It is also observed in Fig that the ANN provided the best accuracy in modelling the emission indices with correlation coefficient of 0.99, 0.99, 0.84 and 0.99 for CO, CO 2, HC and NOx, respectively. Generally, the artificial neural network offers the advantage of being fast, accurate and reliable in the prediction or approximation affairs, especially when numerical and mathematical methods fail. There is also a significant simplicity in using ANN due to its power to deal with multivariate and complicated problems. The experimental results of this study revealed that adding ethanol to gasoline causes to a leaner better combustion. It was experimentally demonstrated that adding 40% ethanol to the blends led to an increase in the engine brake power, torque and brake thermal efficiency, volumetric efficiency and decreases the brake specific fuel consumption. The lean combustion improves the completeness of combustion and therefore the CO emission was expected to be decreased. The experimental results confirmed that by adding more ethanol, the CO was decreased. The oxygen enrichment generated from ethanol increased the oxygen ratio in the charge and lead to lean combustion. CO 2 emission varies with the A/F ratio and CO concentration. As a result, the CO 2 emission increased because of the improved combustion. Unburned HC is a product of incomplete combustion which is related to A/F ratio. It is noted that adding ethanol to the blends reduces the HC emission because of oxygen enhancement. When the combustion process is contiguous to stoichiometric, flame temperature increases, therefore, the NOx emission increased. The ANN predictions for the (a) brake power, (b) engine torque, (c) brake thermal efficiency, (d) volumetric efficiency and (e) brake specific fuel consumption yield a correlation coefficient (R) of 0.999, 0.995, 0.981, and 0.986, respectively. It was observed that the ANN model can predict engine performance and exhaust emissions with correlation coefficient (R) varying from Mean PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 99

28 relative errors (MRE) values were in the spectrum of %, while root mean square errors (RMSE) were seen to be very low. The performance of the network in training is shown in Fig.5.13.which clearly indicates that the best validation performance is at epoch 74.This study demonstrates that ANN approach can be used to meticulously forespeak the SI engine performance and emissions. 5.6 CONCLUSION: Power and torque output of the engine used for the experimental work, showed a substantial rise using all blends. On contrary the BSFC showed a sharp decline when ethanol blends was used in the engine. Experimental investigation also showed an escalation in the BTE and volumetric efficiency. On careful examination through the exhaust gas analyzer the concentration of CO and HC emissions were on the lower side as on compared with the emission when the engine was solely operated on gasoline. Lower emissions resulted because ethanol contained high percentage of oxygen. Talking about the next two emissions in the form of CO 2 and NO x which are considered to be quite lethal showed a sizeable increase when ethanol was introduced with gasoline. The ANN model so developed generated the best correlation coefficient(r) ranging from for all performance parameters and the exhaust emissions. Mean relative errors (MRE) values were in the domain of %, while root mean square errors (RMSE) were very low. R values did not increase when neurons in the hidden layer was more than 20 such as 21, 22, 23 and 24. Therefore, a network with one hidden layer and 20 neurons was selected as the most favourable ANN. Research study and subsequent findings helped us to reach to the conclusion that the ANN approach could be considered as the best feasible way to predict the SI engine performance and engine exhalation in a very accurate manner. The ANN results are very good, R values in this model are very close to one, while root mean square errors (RMSE) were found to be very low. Analysis of the experimental data by the ANN revealed that there is a good correlation between the ANN-predicted results and the experimental data. Therefore ANN proved to be a useful tool for correlation and simulation of engine parameters. ANN provided an accurate and simple approach in the analysis of this complex, multivariate problem, the analysis of the SI engine performance and emissions. It is generally depicted that Artificial Neural Network as a powerful modelling tool, can predict the engine performance and emission parameters even in the nonlinear and sophisticated conditions. The results of this research clearly showed that a three layer feed-forward neural network achieved a desirable mapping between the inputs and outputs of the problem. High values of regression coefficients yielded when setting a regression line for predicted and measured datasets. The performance of proposed network is evaluated by several criteria so it can be applied in the industrial fields as well. PREDICTION USING ARTIFICIAL NEURAL NETWORK Page 100