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Hybridization of the Higher Order Neural Networks with the Evolutionary Optimization Algorithms—An Application to Financial Time Series Forecasting

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Advances in Machine Learning for Big Data Analysis

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 218))

Abstract

Multilayer artificial neural networks have characteristics of structural complexity, computational overhead and black box visualization. In contrast, higher order neural networks (HONNs) have single layer trainable weights, fast learning properties, stronger approximation, and higher fault tolerance capability. This chapter discusses about popular HONNs such as Pi-Sigma neural network (PSNN), Sigma-Pi neural network (SPNN), and functional link artificial neural network (FLANN), their architecture, learning process, and applications to financial time series forecasting. To eradicate the limitations of gradient descent-based training, we discuss few evolutionary optimization algorithms for HONN training. The hybrid models obtained are applied to forecast financial time series. Performance analysis is carried out to establish the suitability of evolutionary optimization-based HONNs.

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Author information

Authors and Affiliations

  1. Department of Artificial Intelligence and Machine Learning, CMR College of Engineering & Technology, Hyderabad, 501401, India

    Sarat Chandra Nayak

  2. Department of Information Technology, Silicon Institute of Technology, Bhubaneswar, 751024, India

    Bijan Bihari Misra

  3. Department of Information and Communication Technology, Fakir Mohan University, Balasore, Odisha, India

    Satchidananda Dehuri

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  1. Sarat Chandra Nayak
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  2. Bijan Bihari Misra
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  3. Satchidananda Dehuri
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Editors and Affiliations

  1. Department of Information and Communication Technology, Fakir Mohan University, Balasore, India

    Satchidananda Dehuri

  2. College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan

    Yen-Wei Chen

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Nayak, S.C., Misra, B.B., Dehuri, S. (2022). Hybridization of the Higher Order Neural Networks with the Evolutionary Optimization Algorithms—An Application to Financial Time Series Forecasting. In: Dehuri, S., Chen, YW. (eds) Advances in Machine Learning for Big Data Analysis. Intelligent Systems Reference Library, vol 218. Springer, Singapore. https://doi.org/10.1007/978-981-16-8930-7_5

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