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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Neural Network Training Based on FPGA with Floating Point Number Format and It’s Performance

In this paper, two-layered feed forward artificial neural network’s (ANN) training by back propagation and its implementation on FPGA (field programmable gate array) using floating point number format with different bit lengths are remarked based on EX-OR problem. In the study, being

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