Learning based on fault injection and weight restriction for fault-tolerant hopfield neural networks

Kamiura, Naotake; Isokawa, Teijiro; Matsui, Nobuyuki

doi:10.1109/dftvs.2004.1347858

Cited by 5 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where E(ϕ l ) is an estimation error function defined by (9). If a fit crossover point cannot be found, j = β is used as the crossover point.…”

Section: B Single-point Crossovermentioning

confidence: 99%

“…Fuzzy neural networks (FNNs) have been used in many applications, especially in identification of unknown systems. In nonlinear system identification, FNNs can effectively fit the nonlinear system by calculating the optimized coefficients of the learning mechanism [6]- [9]. But the traditional multiple-input-multipleoutput fuzzy neural network (MIMOFNN) cannot directly be used when there are a large number of input variables.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Merged Fuzzy Neural Network and Its Applications in Battery State-of-Charge Estimation

Wang

et al. 2007

IEEE Trans. On Energy Conversion

159

View full text Add to dashboard Cite

To solve learning problems with vast number of inputs, this paper proposes a novel learning structure merging a number of small fuzzy neural networks (FNNs) into a hierarchical learning structure called a merged-FNN. In this paper, the merged-FNN is proved to be a universal approximator. This computing approach uses a fusion of FNNs using B-spline membership functions (BMFs) with a reduced-form genetic algorithm (RGA). RGA is employed to tune all free parameters of the merged-FNN, including both the control points of the BMFs and the weights of the small FNNs. The merged-FNN can approximate a continuous nonlinear function to any desired degree of accuracy. For a practical application, a battery state-of-charge (BSOC) estimator, which is a twelve input, one output system, in a lithium-ion battery string is proposed to verify the effectiveness of the merged-FNN. From experimental results, the learning ability of the newly proposed merged-FNN with RGA is superior to that of the traditional neural networks with back-propagation learning. Index Terms-Battery state-of-charge (BSOC), battery string, B-spline membership functions (BMFs), fuzzy neural networks (FNNs), merged-FNN, reduced-form genetic algorithm (RGA).

show abstract

“…where E(ϕ l ) is an estimation error function defined by (9). If a fit crossover point cannot be found, j = β is used as the crossover point.…”

Section: B Single-point Crossovermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%