Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
The DSP decision: fixed point or floating?
IEEE Spectrum
The implementation of fuzzy systems, neural networks and fuzzy neural networks using
Information Sciences: an International Journal
Neural Information Processing and VLSI
Neural Information Processing and VLSI
FPGA Implementation of a Neural Network for a Real-Time Hand Tracking System
DELTA '02 Proceedings of the The First IEEE International Workshop on Electronic Design, Test and Applications (DELTA '02)
Design of an FPGA Based Adaptive Neural Controller for Intelligent Robot Navigation
DSD '02 Proceedings of the Euromicro Symposium on Digital Systems Design
An ART-based fuzzy adaptive learning control network
IEEE Transactions on Fuzzy Systems
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive
IEEE Transactions on Fuzzy Systems
FPGA implementation of a wavelet neural network with particle swarm optimization learning
Mathematical and Computer Modelling: An International Journal
Learning rules for neuro-controller via simultaneous perturbation
IEEE Transactions on Neural Networks
Compensatory neurofuzzy systems with fast learning algorithms
IEEE Transactions on Neural Networks
Subsethood-product fuzzy neural inference system (SuPFuNIS)
IEEE Transactions on Neural Networks
Compact yet efficient hardware implementation of artificial neural networks with customized topology
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The implementation of adaptive neural fuzzy networks (NFNs) using field programmable gate arrays (FPGA) is proposed in this study. Hardware implementation of NFNs with learning ability is very difficult. The backpropagation (BP) method in the learning algorithm is widely used in NFNs, making it difficult to implement NFNs in hardware because calculating the backpropagation error of all parameters in a system is very complex. However, we use the simultaneous perturbation method as a learning scheme for the NFN hardware implementation. In order to reduce the chip area, we utilize the traditional non-linear activation function to implement the Gaussian function. We can confirm the reasonableness of NFN performance through some examples.