Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11889/8134
Title: | Evolving neural networks using bird swarm algorithm for data classification and regression applications | Authors: | Aljarah, Ibrahim Faris, Hossam Mirjalili, Seyedali Al-Madi, Nailah Sheta, Alaa Mafarja, Majdi |
Keywords: | Computer algorithms;Neural networks (Computer science) - Optimization;Computer simulation;Wireless sensor networks;Computational intelligence;Bird Swarm Algorithm;Classification | Issue Date: | 2019 | Abstract: | This work proposes a new evolutionary multilayer perceptron neural networks using the recently proposed Bird Swarm Algorithm. The problem of finding the optimal connection weights and neuron biases is first formulated as a minimization problem with mean square error as the objective function. The BSA is then used to estimate the global optimum for this problem. A comprehensive comparative study is conducted using 13 classification datasets, three function approximation datasets, and one real-world case study (Tennessee Eastman chemical reactor problem) to benchmark the performance of the proposed evolutionary neural network. The results are compared with well-regarded conventional and evolutionary trainers and show that the proposed method provides very competitive results. The paper also considers a deep analysis of the results, revealing the flexibility, robustness, and reliability of the proposed trainer when applied to different datasets. | URI: | http://hdl.handle.net/20.500.11889/8134 |
Appears in Collections: | Fulltext Publications |
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Evolving-neural-networks-using-bird-swarm-algorithm-for-data-classification-and-regression-applicationsCluster-Computing.pdf | 3.28 MB | Adobe PDF | View/Open |
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