By Huang D.-S., Zhao Z., Bevilacqua V., Figueroa J.C. (eds.)

This booklet constitutes the refereed complaints of the sixth foreign convention on clever Computing, ICIC 2010, held in Changsha, China, in August 2010. The eighty five revised complete papers provided have been conscientiously reviewed and chosen from a quite a few submissions. The papers are prepared in topical sections on neural networks, evolutionary studying & genetic algorithms, fuzzy concept and types, fuzzy structures and delicate computing, particle swarm optimization and area of interest know-how, supervised & semi-supervised studying, unsupervised & reinforcement studying, combinatorial & numerical optimization, structures biology and computational biology, neural computing and optimization, nature encouraged computing and optimization, wisdom discovery and information mining, synthetic existence and synthetic immune structures, clever computing in picture processing, distinctive consultation on new hand dependent biometric tools, targeted consultation on contemporary advances in photo segmentation, detailed consultation on theories and functions in complex clever computing, certain consultation on seek established software program engineering, certain consultation on bio-inspired computing and purposes, specified consultation on strengthen in dimensionality aid equipment and its functions, exact consultation on protein and gene bioinformatics: tools and functions

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**Extra info for Advanced intelligent computing theories and applications : 6th International Conference on Intelligent Computing, ICIC 2010, Changsha, China, August 18-21, 2010. Proceedings**

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Consequently, it can be conclude that ICA-CMAC performs much faster than conventional CMAC. Direct Inverse Model Control Based on a New Improved CMAC Neural Network 31 Fig. 4. Simulation result of ICA-CMAC Fig. 5. Simulation result of conventional CMAC From the simulations of sine wave signal and step signal, it can be seen that ICA-CMAC applied in direct inverse model control of CSTR can accelerate the convergence to achieve the needs of real-time control. 6 Conclusions Inverse control combined with neural network can achieve the dynamic control of the nonlinear system.

1 Training Method The library matrix F is equal to [ f 1 f2 " f N ] which is formed by N complex patterns of length L: f 1 , f 2 , " , f N . Then the interconnect matrix, T, of EAPNN can be obtained by the following weight-learning method: Further Research on Extended Alternating Projection Neural Network 35 (a) Let the interconnect matrix T equal 0, i ← 1; (b) ε i = ( I − T ) f i , where I is L × L identity matrix, f i is complex pattern used for training the neural network; (c) if ε i =0, then f i is already in the subspace T and jump to (d) else T ← T + ε i ε iH / ε iH ε i ; (d) i ←i +1, if i > N then end, else jump to (b).

1. Testing Accuracy of 4 algorithms Fig. 2. Training Time of 4 algorithms for for Narendra System Narendra System The performance comparison in terms of necessary hidden nodes between ELM-TV and EM-ELM-TV, has also been conducted in these time-variant systems. The target training RMSE(dB) are set as: -40 for perceptron system, -35 for MLP system and -18 for Narendra system. The optimal number of hidden nodes for ELM-TV is obtained by trial-and-error. Table 1 displays performance evaluation between ELM-TV and EM-ELM-TV, since I-ELM-TV and EM-ELMTV are not able to reach the training goals of them within 500 nodes.