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This paper presents the tuning of the structure and parameters of a neural network using an improved genetic algorithm (GA). It is also shown that the improved GA performs better than the standard GA based on some benchmark test functions. A neural network with switches introduced to its links is proposed. By doing this, the proposed neural network can(More)
—It is well known that sliding-mode control can give good transient performance and system robustness. However, the presence of chattering may introduce problems to the actuators. Many chattering elimination methods use a finite dc gain controller which leads to a finite steady-state error. One method to ensure zero steady-state error is using a(More)
Recently we have proposed a simple circuit of winner-take-all (WTA) neural network. Assuming no external input, we have derived an analytic equation for its network response time. In this paper, we further analyze the network response time for a class of winner-take-all circuits involving self-decay and show that the network response time of such a class of(More)
—This paper presents the stability analysis of a fuzzy-model-based control system consisting of a nonlinear plant and a nonlinear state feedback controller and the design of the non-linear gains of the controller. The nonlinear plant is represented by a fuzzy model having rules. A nonlinear state feedback controller is designed to close the feedback loop.(More)
—This paper presents a neural network with a novel neuron model. In this model, the neuron has two activation functions and exhibits a node-to-node relationship in the hidden layer. This neural network provides better performance than a traditional feedforward neural network, and fewer hidden nodes are needed. The parameters of the proposed neural network(More)