Adaptive self-tuning neurocontrol
Mathematics and Computers in Simulation - Special issue from the IMACS/IFAC international symposium on soft computing methods and applications: “SOFTCOM '99” (held in Athens, Greece)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Adaptive Neural Network Control of Robotic Manipulators
Adaptive Neural Network Control of Robotic Manipulators
Short-term Load Forecasting Model Using Fuzzy C Means Based Radial Basis Function Network
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
Fast learning in networks of locally-tuned processing units
Neural Computation
Adaptive neural network control of nonlinear systems by state andoutput feedback
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, the hardware design of Radial Basis Function Neural Network (RBFNN), which is capable of dealing with floating point arithmetic operations and hardware architecture to calculate the centres of hidden layer using the k-means algorithm are proposed. The RBFNN are very much useful in adaptive control applications. Hardware implementation of neural network will give much faster training than traditional processors and also relatively inexpensive. The architecture of RBFNN is based on a computational model whose main features are: the capability to exploit the inherent parallelism of neural networks and to increase or decrease the number of neurons, aiming flexibility of the network. The design has been done with Very High Speed Integrated Circuit Hardware Description Language (VHDL). The results are verified and analysed in the MATLAB environment. In this work, the floating point hardware gives best precision and also very much useful for wide dynamic range requirements. The design was tested and synthesised with the help of Virtex-II pro device. The simulation and synthesis results show the effectiveness and speed of training.