Prem Junsawang¹, Zulqurnain Sabir², Muhammad Asif Zahoor Raja³, Soheil Salahshour⁴, Thongchai Botmart^5,*, Wajaree Weera³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 249-263, 2022, DOI:10.32604/cmc.2022.028513 - 18 May 2022

Abstract The aim of these investigations is to find the numerical performances of the delay differential two-prey and one-predator system. The delay differential models are very significant and always difficult to solve the dynamical kind of ecological nonlinear two-prey and one-predator system. Therefore, a stochastic numerical paradigm based artificial neural network (ANN) along with the Levenberg-Marquardt backpropagation (L-MB) neural networks (NNs), i.e., L-MBNNs is proposed to solve the dynamical two-prey and one-predator model. Three different cases based on the dynamical two-prey and one-predator system have been discussed to check the correctness of the L-MBNNs. The statistic More >

Prem Junswang¹, Zulqurnain Sabir², Muhammad Asif Zahoor Raja³, Waleed Adel^4,5, Thongchai Botmart^6,*, Wajaree Weera⁶

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5015-5030, 2022, DOI:10.32604/cmc.2022.027523 - 21 April 2022

Abstract The purpose of this paper is to present a numerical approach based on the artificial neural networks (ANNs) for solving a novel fractional chaotic financial model that represents the effect of memory and chaos in the presented system. The method is constructed with the combination of the ANNs along with the Levenberg-Marquardt backpropagation (LMB), named the ANNs-LMB. This technique is tested for solving the novel problem for three cases of the fractional-order values and the obtained results are compared with the reference solution. Fifteen numbers neurons have been used to solve the fractional-order chaotic financial More >

Zulqurnain Sabir¹, Manoj Gupta², Muhammad Asif Zahoor Raja³, N. Seshagiri Rao⁴, Muhammad Mubashar Hussain⁵, Faisal Alanazi⁶, Orawit Thinnukool⁷, Pattaraporn Khuwuthyakorn^7,*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1627-1644, 2022, DOI:10.32604/cmc.2022.021462 - 24 February 2022

Abstract The purpose of the current investigations is to solve the nonlinear dynamics based on the nervous stomach model (NSM) using the supervised neural networks (SNNs) along with the novel features of Levenberg-Marquardt backpropagation technique (LMBT), i.e., SNNs-LMBT. The SNNs-LMBT is implemented with three different types of sample data, authentication, testing and training. The ratios for these statistics to solve three different variants of the nonlinear dynamics of the NSM are designated 75% for training, 15% for validation and 10% for testing, respectively. For the numerical measures of the nonlinear dynamics of the NSM, the Runge-Kutta More >

Raissa Uskenbayeva¹, Aigerim Altayeva^1,*, Faryda Gusmanova², Gluyssya Abdulkarimova³, Saule Berkimbaeva⁴, Kuralay Dalbekova⁴, Azizah Suiman⁵, Akzhunis Zhanseitova⁶, Aliya Amreyeva²

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3837-3853, 2022, DOI:10.32604/cmc.2022.020491 - 27 September 2021

Abstract Providing comfortable indoor air quality control in residential construction is an exceedingly important issue. This is due to the structure of the fast response controller of air quality. The presented work shows the breakdown and creation of a mathematical model for an interactive, nonlinear system for the required comfortable air quality. Furthermore, the paper refers to designing traditional proportional integral derivative regulators and proportional, integral, derivative regulators with independent parameters based on a backpropagation neural network. In the end, we perform the experimental outputs of a suggested backpropagation neural network-based proportional, integral, derivative controller and More >

Divneet Singh Kapoor^1,*, Amit Kumar Kohli²

Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 439-446, 2020, DOI:10.32604/iasc.2020.013920

Abstract This research paper represents an intelligent receiver based on the artificial-neuralnetworks (ANNs) for a 4x1 space-frequency-block-coded orthogonal-frequencydivision-multiplexing (SFBC-OFDM) system, working under slow time-varying frequency-selective fading channels. The proposed equalizer directly recovers transmitted symbols from the received signal, without the explicit requirement of the channel estimation. The ANN based equalizer is modelled by using feedforward as well as the recurrent neural-network (NN) architectures, and is trained using error backpropagation algorithms. The major focus is on efficiency and efficacy of three different strategies, namely the gradient-descent with momentum (GDM), resilient-propagation (RProp), and Levenberg-Marquardt (LM) algorithms. The recurrent More >