Research On Differential Evolution-Immune And Clustering Algorithm Of RBF Neural Network Training Algorithm

Radial basis function (RBF) neural network, which has a profound physiological basis, a simple network architecture, fast learning algorithms, good approximation capability, has many successful applications in fields, such as function approximation, pattern recognition, signal processing, system identification, and so on. Artificial immune system(AIS) is a new research field in recent years, which is applied in machine learning ,combine optimization ,information safety ,fault diagnosis, etc ,and it has displayed a strong ability on information processing and problem solving. The application of combining AIS to artificial neural network becomes the research heat. At the same time , more and more researchers begin to design the neural network based on evolutionary computation. In order to improve the performance of the network, the selection of RBF hidden node centers is one of the most important problems in RBF network learning. In this thesis, the existing RBF learning algorithms are investigated, differential evolution is combined with immune algorithm , a revised fuzzy clustering is proposed.The results are as follows:1. Analyzes the virtues and disadvantages of the existing RBF learning algorithms, in the selection of hidden node centers, determination of the hidden node width, and the optimization of the weights.2. By combining the differential evolution with immune algorithm, the differential evolution-immune algorithm is proposed, which chooses the hidden node centers rationally, and show strong performance.3. By combining subtractive clustering with fuzzy c-means clustering, a revised fuzzy clustering algorithm is proposed, which not only inhances robustness of the RBF, but also shortens the training time.4. The differential evolution-immune algorithm and the revised fuzzy clustering algorithm are applied to function approximation and time series prediction problems, the effectiveness of the two algorithms asr proved.Finally, the research work involved in the thesis is summarized and the future developments of RBF are forecast.