RETRACTED: The quadriceps muscle of knee joint modelling Using Hybrid Particle Swarm Optimization-Neural Network (PSO-NN)

Abstract

Neural framework has for quite a while been known for its ability to handle a complex nonlinear system without a logical model and can learn refined nonlinear associations gives. Theoretically, the most surely understood computation to set up the framework is the backpropagation (BP) count which relies on upon the minimization of the mean square error (MSE). However, this algorithm is not totally efficient in the presence of outliers which usually exist in dynamic data. This paper exhibits the modelling of quadriceps muscle model by utilizing counterfeit smart procedures named consolidated backpropagation neural network nonlinear autoregressive (BPNN-NAR) and backpropagation neural network nonlinear autoregressive moving average (BPNN-NARMA) models in view of utilitarian electrical incitement (FES). We adapted particle swarm optimization (PSO) approach to enhance the performance of backpropagation algorithm. In this research, a progression of tests utilizing FES was led. The information that is gotten is utilized to build up the quadriceps muscle model. 934 preparing information, 200 testing and 200 approval information set are utilized as a part of the improvement of muscle model. It was found that both BPNN-NAR and BPNN-NARMA performed well in modelling this type of data. As a conclusion, the neural network time series models performed reasonably efficient for non-linear modelling such as active properties of the quadriceps muscle with one input, namely output namely muscle force.

[Retracted] Information System Security Evaluation Algorithm Based on PSO‐BP Neural Network

“…Both of them are easy to fall into local minimum and affect global optimization. Generally speaking, as long as the weights and thresholds of the neural network can be optimized, the problem of local minimum can be avoided [ 23 , 24 ]. Part of the research combines genetic algorithm with BP neural network using the powerful macro search ability and good global optimization performance of genetic algorithm.…”

Fitting Conventional Neural Network Time Series Models on Sand Price Indices Dataset

“…From the results, obviously that there is no need to adjust NAR and NARMA models when handling data with zero outliers problem. However, there is a need to modify NAR and NARMA models so that they may handle outliers issue effectively, or else the development of NAR model to NARMA demonstrate should not be proceeded since the NARMA model will tend convey greater mistakes, and the results are not strong for additionally use [28][29][30][31][32][33][34][35][36].…”