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Small-world Theory and Application on Neural Network Forecasting

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Tutor: WangShuangXin
School: Beijing Jiaotong University
Course: Detection Technology and Automation
Keywords: Small-world neural network,improvement of BP algorithm,small-world optimization
CLC: O157.5
Type: Master's thesis
Year:  2012
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Abstract:
Small-world network is one of forefront subjects and academic focus in the research area, which has been widely used in many fields such as social networks, Internet networks, biological products, transport networks and so on. On one side, small-world neural network has been proved to have a better performance than regular neural network, but many defects still exist; on the other side, application of small-world optimization algorithm in neural network are still rarely reported. Aiming at what is mentioned above, the main contributions of this thesis are listed as follows:1. Small-world network theories and its important parameters were expatiated fully and systematically. By analyzing and summarizing currently available small-world neural networks, generally existed problems in them were discussed in depth. These efforts were helpful for further study on the improvement and innovation of small-world neural network.2. A novel multilayer feedforward small-world neural network based on connecting optimizition was proposed. Simulation results showed that novel network model presented a better performance of fast convergence rates, small iteration times and strong stability on comparison with different kinds of existed small-world neural networks.3. Optimization algorithms in RBF neural network were analyzed, and method of using small-world optimization algorithm in RBF neural network was proposed. Experiments verified a better performance in parameter researching of RBF neural network.4. Aiming at problems of wind power forecasting, existed techniques were summarized and statistical prediction methods based on neural network were analyzed in order to choose a better one. Based on correlation analysis, history data which had a strong correlation were extracted as input of neural network. Small-world neural network model and RBF neural network using small-world optimization algorithm were operated in20min to4h wind power real-time forecasting respectively. Simulation showed good performance of two models mentioned above.
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