This paper constructs fully parallel NN hardware realization of Artificial Neural Network (ANN) depends on the efficient execution of single neuron. Field Programmable Gate Array (FPGA) reconfigurable computing architecture is appropriated for hardware achievement of ANN. Numerous implementation of ANNs have been reported in scientific documents, trying to reduce Neural Networks NNs hardware ci...

This paper describes a PCI bus based implementation of a binary correlation matrix memory (CMM) neural network and its application and performance for use as a k-NN based pattern classification system. The system expands on earlier VME based system incorporating FPGA based implementation through greater integration and lower cost. Experimental results for several benchmarks show that, compared ...

In this paper a novel neural network (NN) backstepping controller is modified for application to an industrial motor drive system. A control system structure and NN tuning algorithms are presented that are shown to guarantee stability and performance of the closed-loop system. The NN backstepping controller is implemented on an actual motor drive system using a two-PC control system developed a...

This dissertation uses the theory of stochastic arithmetic as a solution for the FPGA implementation of complex control algorithms for power electronics applications. Compared with the traditional digital implementation, the stochastic approach simplifies the computation involved and saves digital resources. The implementation of stochastic arithmetic is also compatible with modern VLSI design ...

In this paper, a smart NN control scheme is proposed. This scheme is designed such that the current control action can utilize the knowledge that the NN learned from the past control process. A chaotic signal is employed as the reference signal to improve the generalization ability of the NN in the training phase of the scheme, where the complex chaotic signal offers much more information for N...

We present an artificial neural-network(NN-) based smart interface framework for sensors operating in harsh environments. The NN-based sensor can automatically compensate for the nonlinear response characteristics and its nonlinear dependency on the environmental parameters, with high accuracy. To show the potential of the proposed NN-based framework, we provide results of a smart capacitive pr...

A new robust learning controller for simultaneous position/force control of constrained uncertain rigid-link electrically-driven (RLED) manipulators is presented. In the controller, a robust nonlinear control design and the direct adaptive neural networks (NN) technique are integrated together. Firstly, NN devices are used to adaptively learn those RLED manipulator’s structured/unstructured unc...

In this paper, we propose a method to design a neural network(NN) by using a genetic algorithm(GA) and simulated annealing(SA). And also, in order to demonstrate the effectiveness of the proposed scheme, we apply the proposed scheme to a coin recognition example. In general, as a problem becomes complex and large-scale, the number of operations increases and hardware implementation to real syst...

conclusions by comparing the results of classification using multiple classifier fusion with respect to using each classifier separately, it is found that the classifier fusion is more effective in enhancing the detection accuracy. objectives through the improvement of classification accuracy rate, this work aims to present a computer-assisted diagnosis system for malaria parasite. materials an...