A fuzzified neural network copes with fuzzy signals and/or weights so that the information about the uncertainty of input and output can be served in the training process. Since learning process is the main function of fuzzy neural networks, in this study, we focus on review and comparison of the existing learning algorithms, so that the theoretical achievement and the application agenda of eac...

A soft sensor is presented that approximates certain health parameters of automotive rechargeable batteries from on-vehicle measurements of current and voltage. The sensor is based on a model of the open circuit voltage curve. This last model is implemented through monotonic neural networks and estimate over-potentials arising from the evolution in time of the Lithium concentration in the elect...

In this paper we investigate the use of an approximate minimum bit error rate (MBER) approach to multiuser detection using recurrent neural networks (RNN). We examine a stochastic gradient adaptive algorithm for approximating the MBER from training data using RNN structures. A comparative analysis of linear and neural multiuser receivers (MUD), employing minimum mean squared error (MMSE) and ap...

This paper studies traffic variable estimation, and presents a method of estimation for the number of vehicle waiting for queue (NVWQ) based on neuro-fuzzy at urban intersection. we present results of training the neural network for a detectorized intersection in Changsha City. The accuracy of NVWQ estimation using the fuzzy neural networks approaches is more than 90%. The fuzzy neural networks...

Recurrent neural networks and hidden Markov models have been the popular tools for sequence recognition problems such as automatic speech recognition. This work investigates the combination of recurrent neural networks and hidden Markov models into the hybrid architecture. This combination is feasible due to the similarity of the architectural dynamics of the two systems. Initial experiments we...

This paper introduces an improved electromagnetism-like algorithm (IEM) for recurrent fuzzy neural controller design. The hybrid IEM algorithm combines the advantages of the electromagnetism-like (EM) algorithm and the genetic algorithm (GA). The proposed IEM is composed of initialization, local search, total force calculation, movement, and evaluation. For recurrent fuzzy neural controller des...