Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> A. Engine Fault Diagnosis <s> In this paper, a fault diagnosis method based on wavelet analysis and neural network combining loosely has been brought. With the wavelet packet decomposition results of the signal as a neural network input value, using the genetic algorithm to optimize the parameters of neural network globally, and finally we can use the trained neural network for fault diagnosis. The simulation results show that this method has a higher computing speed and accuracy than the quasi-Newton algorithm. The method is applied to automobile engine fault diagnosis, and the result confirmed its feasibility and effectiveness. <s> BIB001 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> A. Engine Fault Diagnosis <s> The characteristic vector of cylinder vibration signal is extracted by wavelet packet decomposition. The factor of selection is proposed to take adaptive integration on basis of improved super parent one dependence estimator Bayesian method and back propagation genetic algorithm neural network method. Experimental results on WD615 diesel engine showed that the method has high accuracy rate of detection. <s> BIB002 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> A. Engine Fault Diagnosis <s> according to the stability of the membership degree and low identification rate in fuzzy inference system, this dissertation proposes the application of adaptive neural network-based fuzzy inference system to engine error diagnosis. To reduce the impact of excessive parameters on classification accuracy and cost, it also raises an asynchronous parallel particle swarm optimization method applied to the selection of feature subset. The method use uniform mutation operator, balancing effectively the particles' ability to search globally and to develop. The asynchronous parallel particle swarm optimization algorithm(AP-PSO) that is used to select the feature subset is a potential feature subset that carries the characteristic of firstly initializing every particle as the selection question. Then, the method adopts improved asynchronous parallel particle swarm algorithm to conduct optimal searching based on the particle swarm that include several feature subsets and evaluate the classification ability (adaptive value) of the feature subset selected by way of Support Vector Machine. Finally, the optimal feature subset is got. Through verification of the build diagnosis model with data of engine tests, it has been found that the recognition accuracy attain to 98.72%, training error falling to 0.004423.The experiment indicates that the recognition rate of ANFIS system is significantly better than independent neural network reasoning system, fuzzy inference system. <s> BIB003 </s> Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey <s> A. Engine Fault Diagnosis <s> This paper, in order to reduce fault and improve ratio of recognition, build adaptive neural network-based fuzzy inference system (ANFIS), which was applied to build a fault diagnosis model of automobile engine, adopts the method of information fusion in entropy method to optimize the input interface. To reduce the impact of excessive parameters on classification accuracy and cost, it also raises an asynchronous parallel particle swarm optimization method applied to the selection of feature subset. By using gradient descent genetic algorithm and optimization of system parameters of neutral network learning algorithm, so as to speed up learning. Through verification of the build diagnosis model with data of engine tests, it has been found that the recognition accuracy attain to 97.39%, training error falling to 0.001702. The experiment indicates that gradient descent genetic algorithm is a fast algorithm that can support the local optimization of individual chromosome and the global optimization of chromosomes in a group. <s> BIB004
A fault diagnosis method was proposed in BIB001 , which used the wavelet transform to decompose the engine signal and then feed the decomposed components to a neural network. A genetic algorithm was then used to optimize the parameters of the neural network. The fault diagnosis problem of the vibration parameter is addressed in BIB002 , where an adaptive neural network-based fuzzy inference system was developed. A hybrid gradient descent genetic algorithm was used to speed up the learning process. The experimental results provided in this paper showed that the method outperformed other neural networks in stability, recognition rate, and fitting capability. To reduce the number of inputs to an engine model, in BIB003 and BIB004 , a parallel PSO algorithm was adopted to the selection of a feature subset. A uniform mutation operator was used in the optimization algorithm to balance the particles search ability. Using this method, they showed that the fault recognition system could reach 98.72% accuracy BIB003 , BIB004 .