In this paper a time domain recursive digital filter model, based on recurrent neural network is proposed. This problem can be considered as a training procedure of two layer recurrent neural network. The proposed neural network training algorithm is based on determination of the sensitivity coefficients of the recurrent system. The dynamic model of two layer recurrent neural network described ...

This paper compares the performance of Nonlinear Autoregressive Exogenous (NARX) Neural Network and Support Vector Machine (SVM) regression model to predict Air Pollutant Index (API) in Malaysia. Two models namely NARX SVM were developed using API air quality time series data from three monitoring stations: Pasir Gudang, TTDI Jaya Larkin. Hourly parameters collected year 2016 2018 utilized prod...

A novel global hybrid algorithm for feedforward neural networks p. 9 Study on relationship between NIHSS and TCM-SSASD based on the BP neural network multiple models method p. 17 Application of back-propagation neural network to power transformer insulation diagnosis p. 26 Momentum BP neural networks in structural damage detection based on static displacements and natural frequencies p. 35 Defo...

In this paper, multi step ahead prediction of monthly sunspot real time series are carried out. This series is highly chaotic in nature [7]. This paper compares performance of proposed Jordan Elman Neural Network with TLRNN (Time lag recurrent neural network), and RNN (Recurrent neural network) for multi-step ahead (1, 6, 12, 18, 24) predictions. It is seen that the proposed neural network mode...

This paper presents two recurrent neural networks for solving the shortest path problem. Simplifying the architecture of a recurrent neural network based on the primal problem formulation, the first recurrent neural network called the primal routing network has less complex connectivity than its predecessor. Based on the dual problem formulation, the second recurrent neural network called the d...

A recurrent neural network is presented for solving systems of quadratic programming problems with equality constraints involving complex-valued coefficients. The proposed recurrent neural network is asymptotically stable and able to generate optimal solutions to quadratic programs with equality constraints. An opamp based analogue circuit realization of the recurrent neural network is describe...

This study aims at investigation of stimulation by using intra-spinal signals decoded from electrocorticography (ECoG) assessments to restore the movements of the leg in an animal model of spinal cord injury (SCI). The present work comprised of three steps. First, ECoG signals and the associated leg joint changes (hip, knee, and ankle) in sedated healthy rabbits were recorded in different trial...

Neurological diseases may reduce Tibialis Anterior (TA) muscle recruitment capacity causing gait disorders, such as drop foot (DF). The majority of DF patients still retain excitable nerves and muscles which makes Functional Electrical Stimulation (FES) an adequate technique to restore lost mobility. Recent studies suggest the need for developing personalized assist-as-needed control strategies...