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A hybrid learning algorithm for evolving Flexible Beta Basis Function Neural Tree Model
2013
Neurocomputing
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In this paper, a tree-based encoding method is introduced to represent the Beta basis function neural network. ...
The proposed model called Flexible Beta Basis Function Neural Tree (FBBFNT) can be created and optimized based on the predefined Beta operator sets. ...
Ajith Abraham acknowledges the support from the framework of the ...
doi:10.1016/j.neucom.2013.01.024
fatcat:mx6tfujwlfccfon42yi3dmiw7a
Extended immune programming and opposite-based PSO for evolving flexible beta basis function neural tree
2013
2013 IEEE International Conference on Cybernetics (CYBCO)
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In this paper, a new hybrid learning algorithm based on the global optimization techniques, is introduced to evolve the Flexible Beta Basis Function Neural Tree (FBBFNT). ...
The structure is developed using the Extended Immune Programming (EIP) and the Beta parameters and connected weights are optimized using the Opposite-based Particle Swarm Optimization (OPSO) algorithm. ...
Beta basis function neural network. ...
doi:10.1109/cybconf.2013.6617425
dblp:conf/cybconf/BouazizAA13
fatcat:2sf27ntea5gbrkhs2ojhyhzsbe
Hierarchical multi-dimensional differential evolution for the design of beta basis function neural network
2012
Neurocomputing
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This paper proposes a hierarchical multi-dimensional differential evolution (HMDDE) algorithm, which is an automatic computational frame work for the optimization of beta basis function neural network ...
For the beta neural network consisting of m neurons, n individuals (different lengths) are formed in the upper level to optimize the structure of the beta neural network. ...
Acknowledgment The authors would like to acknowledge the financial support of this work by grants from the General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program. ...
doi:10.1016/j.neucom.2012.04.008
fatcat:2zjtia22pfezthd5eilr7x262q
Multi-agent evolutionary design of Flexible Beta Basis Function Neural Tree
2014
2014 International Joint Conference on Neural Networks (IJCNN)
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In the other side, a complex system of Artificial Neural Network called Flexible Beta Basis Function Neural Tree (FBBFNT) has reached a great level in the prediction search domain. ...
Under the awareness of its power, the application of MAS was no more limited to very specific problems, but to almost application area: optimization, neural network, robotics, fuzzy system, etc. ...
CZ.1.05/1.1.00/02.0070 by operational program 'Research and Development for Innovations' funded by the Structural Funds of the European Union and state budget of the Czech Republic, EU. ...
doi:10.1109/ijcnn.2014.6889726
dblp:conf/ijcnn/AmmarBAA14
fatcat:gnrmyb3adjc6heyvgo6ukah52e
Hierarchical winner-take-all particle swarm optimization social network for neural model fitting
2016
Journal of Computational Neuroscience
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Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. ...
The topology of a PSO social network is a major contributor to optimization success. ...
Coventry Hierarchical architecture for the winner-take-all particle swarm social network. A.) The overall topology of the WTAPSO social network. ...
doi:10.1007/s10827-016-0628-2
pmid:27726048
pmcid:PMC5253113
fatcat:cihbxvti5famxjm3bwwgt52lce
Classification of Mental Tasks using EEG and Hierarchical Classifier employing Optimised Neural Networks
2016
International Journal of Computer Applications
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The adaptation of network weights using Comprehensive Learning Particle Swarm Optimization (CLPSO) is proposed to improve the performance of Neural Network (NN). ...
Results obtained using the proposed methods are compared with other contemporary methods such as Linear Discrimination analysis (LDA), neural networks based on improved particle swarm optimization (IPSONN ...
Particle Swarm Optimization Particle swarm optimization (PSO) is a robust nature inspired algorithm for solving optimization problems based on movement and intelligence of swarms [29] . ...
doi:10.5120/ijca2016908163
fatcat:xlfg4nb5dvaqlnxwzbulu3qg3y
Design and Analysis of Optimization based Integrated ANFIS- PID Controller for Networked Controlled Systems (NCSs)
2020
Cogent Engineering
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The simulation of the proposed approach is processed for both with and without time delay consideration to perform analysis over Peak Overshoot, Settling Time and Rise Time of the network. ...
In order to enhance the performance of the network, various soft computing techniques were presented in previous works for both scenarios i.e. with delay and without delay, in which Fuzzy-PID controllers ...
Algorithm, Particle Swarm Optimization, Grey Wolf Optimization and so forth. ...
doi:10.1080/23311916.2020.1772944
fatcat:n47i2avxknd4lluo5u5vups6du
PSO-based update memory for Improved Harmony Search algorithm to the evolution of FBBFNT' parameters
2014
2014 IEEE Congress on Evolutionary Computation (CEC)
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In this paper, a PSO-based update memory for Improved Harmony Search (PSOUM-IHS) algorithm is proposed to learn the parameters of Flexible Beta Basis Function Neural Tree (FBBFNT) model. ...
The performance of the proposed evolving neural network is evaluated for nonlinear systems of prediction and identification and then compared with those of related models. ...
FLEXIBLE BETA BASIS FUNCTION NEURAL TREE MODEL The initiative of using Beta function for designing Artificial Neural Network was introduced by Alimi [16] . ...
doi:10.1109/cec.2014.6900304
dblp:conf/cec/BouazizAA14
fatcat:373vh64xjbawndxjq7pzu5dumm
Engineering Evolutionary Intelligent Systems: Methodologies, Architectures and Reviews
[chapter]
2008
Studies in Computational Intelligence
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In this Chapter, we illustrate the various possibilities for designing intelligent systems using evolutionary algorithms and also present some of the generic evolutionary design architectures that has ...
evolved during the last couple of decades. ...
[6] proposed an evolutionary method for the design of beta basis function neural networks (BBFNN) and beta fuzzy systems (BFS). ...
doi:10.1007/978-3-540-75396-4_1
fatcat:xplkbxzk4vbpbmnp4zxrjomj3q
WEIGHTED GREY WOLF OPTIMIZER WITH IMPROVED CONVERGENCE RATE IN TRAINING MULTI-LAYER PERCEPTRON TO SOLVE CLASSIFICATION PROBLEMS
2021
Jordanian Journal of Computers and Information Technology
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WGWOIC trainer provides the optimal values for weight and biases to the MLP network. ...
Furthermore, the hybridization of the WGWOIC meta-heuristic optimization algorithm with a Multi-Layer Perceptron (MLP) neural network is employed to improve the accuracy of the classification problem. ...
The hierarchical classification diagram of neural networks is depicted in Figure 5 . Yu et al. ...
doi:10.5455/jjcit.71-1621353647
fatcat:uo5kcygei5dqlgi3gwui7an7iy
Nature-Inspired Algorithms Applications to Power System Optimization
2021
Aswan University Journal of Sciences and Technology
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From Darwinian evolution point of view, survival of the fittest will result in the variations and success of species, which can survive and optimally adapt to environments. ...
Different Algorithms are presented for solving power system optimization problems. ...
There are various kinds of neural network mechanisms are explored, feed-forward neural networks, recurrent neural networks, timedelayed neural networks, real-time recurrent neural networks, etc. ...
doi:10.21608/aujst.2021.226479
fatcat:onicsotjlnbpbbepynluyk5wpe
Hierarchical Bi-level Multi-Objective Evolution of Single- and Multi-layer Echo State Network Autoencoders for Data Representations
[article]
2018
arXiv
pre-print
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Multi-objective Particle Swarm Optimization (MOPSO) is used to optimize ESN structure in a way to provide a trade-off between the network complexity decreasing and the accuracy increasing. ...
Echo State Network (ESN) presents a distinguished kind of recurrent neural networks. It is built upon a sparse, random and large hidden infrastructure called reservoir. ...
Acknowledgment The research leading to these results has received funding from the Ministry of Higher Education and Scientific Research of Tunisia under the grant agreement number LR11ES48. ...
arXiv:1806.01016v2
fatcat:z3yfsvmp6jgbtfoemxquom73du
Editorial
2021
Intelligent Data Analysis
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The authors propose the method of optimizing the support vector machine parameters by the cuckoo search algorithm, genetic algorithm and particle swarm optimization algorithm. ...
They report that the linear kernel function would be the best kernel function with a high accuracy rate while the radial basis function is used to optimize the kernel function, which can also improve the ...
doi:10.3233/ida-200017
fatcat:2ymflmeqnjaezgumquc5jhjc6e
Genetically Modified Wolf Optimization with Stochastic Gradient Descent for Optimising Deep Neural Networks
[article]
2023
arXiv
pre-print
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Hence, this research aims to analyze an alternative approach to optimizing neural network (NN) weights, with the use of population-based metaheuristic algorithms. ...
The state-of-the-art method of optimizing the networks is done by using gradient descent algorithms, such as Stochastic Gradient Descent (SGD). ...
This approach generates the optimal networks for the given problem, however, this research aims to design an algorithm which trains the weights of the network. ...
arXiv:2301.08950v1
fatcat:zqsebandovbo5drllv32jehnra
Literature Review on Big Data Analytics Methods
[chapter]
2020
Social Media and Machine Learning
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In this research, the methods of both ML and DL have been discussed, and an ML/DL deployment model for IOT data has been proposed. ...
Also, the format, size, variety, and velocity of generated data bring complexity for industries to apply them in an efficient and effective way. ...
Acknowledgements This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. ...
doi:10.5772/intechopen.86843
fatcat:awwtixbkc5antd5xcmkh7odcpq
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