Application of bacterial foraging technique trained artificial and wavelet neural networks in load forecasting

Ulagammai, M.; Venkatesh, P.; Kannan, P.; Padhy, Narayana Prasad

doi:10.1016/j.neucom.2006.05.020

Cited by 86 publications

(53 citation statements)

References 7 publications

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“…On functions f 5 and f 6 , adaptive BFOA, GSO and PSO yield the exact optimum while GA yielded the approximate optimum. All algorithms come very close to the global optimum on f 7 . Figure 3 For example, the problem f 8 shown in Figure 7 (a), have five extreme, the bottom point at the deepest hole is the global optimal position and the other holes are deceptive.…”

Section: Resultsmentioning

confidence: 77%

“…In 2002, inspired by the researches on the foraging behavior of E. coli bacteria, Prof. K. M. Passino proposed Bacterial Foraging Optimization Algorithm (BFOA) [4], which also has been applied to many engineering problems [5][6][7][8][9]. In the foraging process, if bacteria find no better food in the original direction, it will turn to a new direction.…”

Section: B Social Foragingmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Bacterial Foraging Optimization Algorithm Based on Social Foraging Strategy

Shen¹,

Zhu²

2014

JNW

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Section: Resultsmentioning

confidence: 77%

Section: B Social Foragingmentioning

confidence: 99%

Adaptive Bacterial Foraging Optimization Algorithm Based on Social Foraging Strategy

Shen¹,

Zhu²

2014

JNW

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“…For example, the application of FF, CS, GSO, BFO, and CFO algorithms for the FNN weights optimization is available in [179], [180], [181], [182,183], [184] respectively.…”

Section: Weight Optimizationmentioning

confidence: 99%

Metaheuristic design of feedforward neural networks: A review of two decades of research

Ojha

Abraham

Snel

2017

Engineering Applications of Artificial Intelligence

442

154

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Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era.

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“…in short term load forecasting, (Bashir & El-Hawary, 2000;Benaouda, Murtagh, Starck, & Renaud, 2006;Gao & Tsoukalas, 2001;Ulugammai, Venkatesh, Kannan, & Padhy, 2007;S. J. Yao, Song, Zhang, & Cheng, 2000), in time series prediction, (Cao, et al, 1995;Chen, Yang, & Dong, 2006;Cristea, Tuduce, & Cristea, 2000), signal classification and compression, (Kadambe & Srinivasan, 2006;Pittner, Kamarthi, & Gao, 1998;Subasi, Alkan, Koklukaya, & Kiymik, 2005), signal denoising, (Z.…”

Section: Introductionmentioning

confidence: 99%

Wavelet neural networks: A practical guide

2013

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Abstract-Wavelet networks (WNs) are a new class of networks which have been used with great success in a wide range of application. However a general accepted framework for applying WNs is missing from the literature. In this study, we present a complete statistical model identification framework in order to apply WNs in various applications. The following subjects were thorough examined: the structure of a WN, training methods, initialization algorithms, variable significance and variable selection algorithms, model selection methods and finally methods to construct confidence and prediction intervals. In addition the complexity of each algorithm is discussed. Our proposed framework was tested in two simulated cases, in one chaotic time series described by the Mackey-Glass equation and in three real datasets described by daily temperatures in Berlin, daily wind speeds in New York and breast cancer classification. Our results have shown that the proposed algorithms produce stable and robust results indicating that our proposed framework can be applied in various applications.

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Application of bacterial foraging technique trained artificial and wavelet neural networks in load forecasting

Cited by 86 publications

References 7 publications

Adaptive Bacterial Foraging Optimization Algorithm Based on Social Foraging Strategy

Adaptive Bacterial Foraging Optimization Algorithm Based on Social Foraging Strategy

Metaheuristic design of feedforward neural networks: A review of two decades of research

Wavelet neural networks: A practical guide

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