FPGA Implementation of ANN Training using Levenberg and Marquardt Algorithm

Artificial Neural Network (ANN) training using gradient-based Levenberg & Marqaurdt (LM) algorithm has been implemented on FPGA for the solution of dynamic system identification problems within the scope of the study. In the implementation, IEEE 754 floating-point number format has been used

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FPGA Implementation of Wavelet Neural Network Training with PSO/iPSO

In this study, field-programmable gate array (FPGA)-based hardware implementation of the wavelet neural network (WNN) training using particle swarm optimization (PSO) and improved particle swarm optimization (iPSO) algorithms are presented. The WNN architecture and wavelet activation function approach that is proper for

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FPGA Implementation of Neuro-fuzzy System with Improved PSO Learning

This paper presents the first hardware implementation of neuro-fuzzy system (NFS) with its metaheuristic learning ability on field programmable gate array (FPGA). Metaheuristic learning of NFS for all of its parameters is accomplished by using the improved particle swarm optimization (iPSO). As

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Prediction of surface roughness and cutting zone temperature in turning processes of AISI 304 stainless steel using ANFIS with PSO learning

This paper presents an approach for modeling and prediction of both surface roughness and cutting zone tem￾perature in turning of AISI304 austenitic stainless steel using multi-layer coated (TiCN+TiC+TiCN+TiN) tungsten carbide tools. The proposed approach is based on an adap￾tive neuro-fuzzy inference

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Neural identification of dynamic systems on FPGA with improved PSO learning

This work introduces hardware implementation of artificial neural networks (ANNs) with learning ability on field programmable gate array (FPGA) for dynamic system identification. The learning phase is accomplished by using the improved particle swarm optimization (PSO). The improved PSO is obtained by

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