Titlebook: Advances in Neural Networks - ISNN 2007; 4th International Sy Derong Liu,Shumin Fei,Changyin Sun Conference proceedings 2007 Springer-Verla

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Social Media and Social Computing,models. The objective is to control the conditional PDF of the system output to follow a given target function by using dynamic neural network models. B-spline neural networks are used to model the dynamic output probability density functions (PDFs), then the concerned problem is transferred into th

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Elena Zheleva,Evimaria Terzi,Lise Getoorle neural networks (SNN). The SNN is uniquely determined by the design of the global integral sliding mode surface, the output of which replaces the corrective control, and FNN is applied to mimic the equivalent control. In this scheme, the bounds of the uncertainties and the extern disturbance are

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Elena Zheleva,Evimaria Terzi,Lise Getoor basis function(RBF) neural network. The system uncertainty is approximated by RBF neural networks, and a parameter update law is presented for approximating the system uncertainty. In each step, the control scheme is derived in terms of linear matrix inequalities (LMI’s). A robust adaptive controll

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Elena Zheleva,Evimaria Terzi,Lise Getoortomotive engines. The sliding mode control (SMC) structure is used and a new sliding surface is developed in the paper. The RBF network adaptation and the control law are derived using the Lyapunov method so that the entire system stability and the network convergence are guaranteed. The developed m

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Elena Zheleva,Evimaria Terzi,Lise Getoorel with same fuzzy sets is established. To make the states of the closed-loop system follow those of the reference model, a controller including of neuro-fuzzy adaptive and linear feedback term is designed. The linear feedback parameters can be solved by LMI approach. Adaptive term is used to compen

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Michael J. Zakour,David F. Gillespie algorithm of FNN can adjust not only the connection weights but also the sigmoid function parameters. This makes FNN characterized with online learning and high learning speed. The FNN has the following advantages when applied to temperature control problems: high learning ability, which considerab

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Direct Adaptive Fuzzy-Neural Control for MIMO Nonlinear Systems Via BacksteppingOverview:

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A Novel Cross Layer Power Control Game Algorithm Based on Neural Fuzzy Connection Admission ControllOverview:

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