‎artificial neural networks have the advantages such as learning, ‎adaptation‎, ‎fault-tolerance‎, ‎parallelism and generalization‎. ‎this ‎paper is a scrutiny on the application of diverse learning methods‎ ‎in speed of convergence in neural networks‎. ‎for this aim‎, ‎first we ‎introduce a perceptron method based on artificial neural networks‎ ‎which has been applied for solving a non-singula...

artificial neural networks have the advantages such as learning, adaptation, fault-tolerance, parallelism and generalization. this paper mainly intends to offer a novel method for finding a solution of a fuzzy equation that supposedly has a real solution. for this scope, we applied an architecture of fuzzy neural networks such that the corresponding connection weights are real numbers. the sugg...

introduction: manipulation of protein stability is important for understanding the principles that govern protein thermostability, both in basic research and industrial applications. various data mining techniques exist for prediction of thermostable proteins. furthermore, ann methods have attracted significant attention for prediction of thermostability, because they constitute an appropriate ...

In this paper, we interpret a fuzzy differential equation by using the strongly generalized differentiability concept. Utilizing the Generalized characterization Theorem. Then a novel hybrid method based on learning algorithm of fuzzy neural network for the solution of differential equation with fuzzy initial value is presented. Here neural network is considered as a part of large eld called ne...

The apical delta is an intricate system within the root canal and incompletely debridement may affect the long-term prognosis of root canal therapy. The aim of the present study is to investigate the morphologic features of apical deltas in human teeth with micro-computed tomography (micro-CT) using a centreline-fitting algorithm. One hundred and thirty-six apical deltas were detected in 1400 t...

o enhance the performances of rough-neural networks (R-NNs) in the system identification‎, ‎on the base of emotional learning‎, ‎a new stable learning algorithm is developed for them‎. ‎This algorithm facilitates the error convergence by increasing the memory depth of R-NNs‎. ‎To this end‎, ‎an emotional signal as a linear combination of identification error and its differences is used to achie...

Feature compensation for noise robust speech recognition becomes more effective if normalization of time-derivative parameters is taken into account. This paper describes an implementation of Delta-Cepstrum Normalization (DCN) that runs with only minimum response time. The proposed algorithm, referred to as Recursive DCN, provides word error rate improvements comparable to conventional DCN. Sin...

Cannabinoid receptors in the brain (CB(1)) take part in modulation of learning, and are particularly important for working and short-term memory. Here, we employed a delayed-matching-to-place (DMTP) task in the open-field water maze and examined the effects of cannabis plant extracts rich in either Delta(9)-tetrahydrocannabinol (Delta(9)-THC), or rich in cannabidiol (CBD), on spatial working an...