The Learning Methods Research Of Evolutionary Computation Optimizing Feedforward Neural Network

The research of simulation and modeling for the Complex System is one of a difficulty and a hot point in the System simulation research. The Artificial neural network, represents the network problem-solving knowledge through a large number of neurons which are connected to each other of using weights,has the characteristics of self-learning, parallel processing and also it can approximate any nonlinear function with arbitrary precision. The neural network is suitable for modeling complex systems because of its these advantages.However,there are some problems in the application of the neural network, for example, the minimum of network learning, the slow convergence speed,the compelx of network structure design and the weak of the generalization performance, all of these limitations prevent the neural network from applying in application fields. Neural network learning is essentially the optimization of network structure and weights, these limitations of the neural network will involve the optimal problems of network learning inevitably. The evolutionary computation is one of the artificial intelligence technology to simulate the evolution process and mechanism. The evolutionary computation is intelligent and parallelism of these two characteristics, and it is not affected by the target function which is continuously differentiable constrains,and it will show the unique advantage of this method in the problems of discrete,polymorphism and noise.The advantages of the evolutionary computation provide a new feasible way of solving the defects of neural network.In this paper, we use evolutionary computation of the swarm intelligence to solve the main problems of neural networks in the modeling of complex systems, and also applied to the wood surface defect recognition. Main contents are as follows:First of all, aiming at the problem that back-propagation (BP) learning algorithm is trapped easily in local minima and the BP learning precision is not high, a method for optimizing weights of neural network based on BP and particle swarm optimization is proposed. This method combines particle swarm optimization with back-propagation, and gives a good tradeoff between PSO global exploration and BP local exploitation. More importantly, this hybrid provides an opportunity to interact fully between PSO and BP, that is, BP operation can obtain better solution based on the result of PSO in each generation, meanwhile this solution returns back the PSO swarm and guides the swarm evolution rapidly through sharing its position information for swarm. The two algorithms supplement and promote each other to achieve the optimization purposes. Some simulation experiments show that this method has merits of high precision and rapid convergence speed, and its learning performance outperforms some prevail algorithms as to the neural network with fixed structure. Single evolutionary algorithm has premature convergence, the low optimization efficiency, in complex optimization problems, so a improved hybrid genetic algorithm (IHGA), which is a combination of real-coded genetic algorithm and particle swarm optimization method, has been put forward.IHGA Introduces the idea of ecology niche construction, and makes individuals have the ability to learn. Different search mechanisms of genetic algorithm and particle swarm optimization to generate offspring, can maintain the diversity of the individuals,and to some extent avoid the algorithm premature convergence. Theory proved IHGA to have a probability of a global convergence.Experimental results also show that this method significantly improves the reliability of the algorithm to optimize performance and its optimal solution. On this basis, the neural network with the connection switch has been introduced and a method that simultaneously tunes structure and parameters of the neural network based on IHGA is proposed. The simulation results show that the network learning designed by the method has high precision and structure thrift.Study on the radial basis function network (RBF) design problems, a new design method of RBF network based on PSO has been put forward. The method,which consists of regularized orthogonal least squares method and D-optimal experimental design combined algorithm automatically build the structure frugal RBF network model. Simulation examples show that the method is a better RBF network design method. Improved three learning parameters, base width parameter,regularization parameter,and D-optimal cost of coefficients,which affect generalization performance.The particle swarm optimization is used to search the optimal combination of three important learning parameters, i.e., the RBF width, the regularized parameter and D-optimality weight parameter, which influence the network’s generalization ability.Simulation examples show that the method is a better RBF network design method.Research in the wood defect detection problem, surface defects detection technology has always played an important role. The pattern recognition of surface defection commonly used artificial neural network technology. Compared with other detection technologies, the neural network can improve the accuracy and efficiency of detection, But still can not get rid of the neural network defect limits, which let the recognition rate be low. Finally, the proposed learning method is applied to the wood surface defect recognition and compared with other common network learning methods and analyze the advantages and disadvantages of this method. The results show that the network models constructed using the proposed method has higher recognition accuracy and frugality structure,and in this paper the neural network learning is a suitable method applied to a complex network modeling.