Training Artificial Neural Networks by a Hybrid PSO-CS Algorithm

Chen, Jeng-Fung; Hung, Quang; Hsieh, Ho-Nien

doi:10.3390/a8020292

Cited by 60 publications

(25 citation statements)

References 33 publications

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“…Equation (8) shows that the pendulum is not applied with external torque, therefore zero is assigned to the variable τ 0 .…”

Section: Dynamic System Analysis Of Planetary Train-type Inverted Penmentioning

confidence: 99%

“…Therefore, several algorithms were used to adjust parameters in neural networks and to find the appropriate network structure. Recently, several researchers [4][5][6][7][8][9] have used various evolutionary algorithms to determine the parameters of neural networks. In [4][5][6][7], several researchers have adopted genetic algorithms (GAs), artificial bee colony (ABC), and gray wolf optimization (GWO) to adjust parameters of neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…However, search in GAs is extremely tedious, which is the primary disadvantage of GAs. Similar to GAs, particle swarm optimization (PSO) [8,9] is a robust numerical optimization algorithm. PSO has been successfully used for solving several optimization problems.…”

Section: Introductionmentioning

confidence: 99%

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Unstable System Control Using an Improved Particle Swarm Optimization-Based Neural Network Controller

Lin

Jhang

2019

Electronics

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In this study, an improved particle swarm optimization (IPSO)-based neural network controller (NNC) is proposed for solving a real unstable control problem. The proposed IPSO automatically determines an NNC structure by a hierarchical approach and optimizes the parameters of the NNC by chaos particle swarm optimization. The proposed NNC based on an IPSO learning algorithm is used for controlling a practical planetary train-type inverted pendulum system. Experimental results show that the robustness and effectiveness of the proposed NNC based on IPSO are superior to those of other methods.

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“…Equation (8) shows that the pendulum is not applied with external torque, therefore zero is assigned to the variable τ 0 .…”

Section: Dynamic System Analysis Of Planetary Train-type Inverted Penmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Unstable System Control Using an Improved Particle Swarm Optimization-Based Neural Network Controller

Lin

Jhang

2019

Electronics

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show abstract

“…As a result, multilayer perceptron (MLP) ANNs are proposed, and they are not suffering from the disadvantages of SLP by using one or more than one hidden layers in the Artificial neural networks. As a result, the most used type of ANNs is the Multilayer type [17]. Few of the main powerful features of the MLP are its fault tolerance, non-linearity, robustness to noise, learning capacity, parallelism, and its great ability to generalize [14].…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Grasshopper Optimization - Backpropagation for Feedforward Neural Network Training

Alsammarraie¹,

Hussein²

2020

Tikrit J. Pure Sci.

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The Grasshopper optimization algorithm showed a rapid converge in the initial phases of the global search, however while being around the global optimum, the searching process became so slow. On the contrary, the gradient descending method around achieved faster convergent speed global optimum, and the convergent accuracy was showed to be higher at the same time. As a result, the proposed hybrid algorithm combined Grasshopper optimization algorithm (GOA) along with the back-propagation (BP) algorithm, also referred to as GOA–BP algorithm, was introduced to provide training to the weights of the feed forward neural network (FNN), the proposed hybrid algorithm can utilize the strong global searching ability of the GOA, and the intense local searching ability of the Back-Propagation algorithm. The results of experiments showed that the proposed hybrid GOA–BP algorithm was better and faster in convergent speed and accuracy than the Grasshopper optimization algorithm (GOA) and BP algorithm. http://dx.doi.org/10.25130/tjps.25.2020.018

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“…The evolutionary approaches such as Genetic algorithm, Ant Colony Optimization, artificial bee colony, Cuckoo search, PSO are usually being used in avoiding local minima and improving convergence rate of training algorithm [34][35][36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Training of Multilayer Perceptrons with Improved Particle Swarm Optimization for the Heart Diseases Prediction

Jp¹,

Ss²

2017

Int J Swarm Intel Evol Comput

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IntroductionThe graphical depiction of the heart beats in the form of electrical signals is known as electrocardiogram (ECG). The abnormal rhythms of the heart beats are termed as cardiac arrhythmias. Some arrhythmias are life threatening. Therefore there is need to identify the heart conditions of cardiac patients. The identification of cardiac arrhythmias in early stage can save the patients from sudden cardiac arrest.A variation in the consecutive cardiac beats is referred to as heart rate variability (HRV). By means of HRV analysis technique cardiac health can usually be computed. An estimation of HRV is recently being adopted as investigation tool for recognition of heart abnormalities in cardiology. AbstractThe study of Heart rate variability is recently gained momentum for an estimation of heart health. This paper suggests a new approach for enhancement of the prediction accuracy of Multi-Layer Perceptrons (MLP) neural network using improved Particle Swarm Optimization (IPSO) technique. The IPSO computes the weights and biases of MLP for the more accurate prediction of the cardiac arrhythmia classes. This study for heart condition prediction involves selection of Three types of heart signals including Left Bundle Branch Block (LBBB), Normal Sinus Rhythm (NSR), Right Bundle Branch Block (RBBB) from MIT-BIH arrhythmia database, formation of heart rate time series, extraction of features from RR interval time series, implementation of training algorithm and prediction of arrhythmia classes. Several experiments on the proposed training method are carried out to superior the convergence ability of MLP. The experimental results gives comparably better evaluation over gradient based Back-Propagation (BP) learning algorithm.

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Training Artificial Neural Networks by a Hybrid PSO-CS Algorithm

Cited by 60 publications

References 33 publications

Unstable System Control Using an Improved Particle Swarm Optimization-Based Neural Network Controller

Unstable System Control Using an Improved Particle Swarm Optimization-Based Neural Network Controller

A New Hybrid Grasshopper Optimization - Backpropagation for Feedforward Neural Network Training

Training of Multilayer Perceptrons with Improved Particle Swarm Optimization for the Heart Diseases Prediction

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