A Chebyshev polynomial feedforward neural network trained by differential evolution and its application in environmental case studies. (April 2020)

• Record Type: Journal Article
• Authors: Troumbis, Ioannis A.; Tsekouras, George E.; Tsimikas, John; Kalloniatis, Christos; Haralambopoulos, Dias
• Is Part Of: Environmental modelling & software. Volume 126(2020)
• Publication Date: 2020-04
• Subjects: Polynomial neural networks -- Chebyshev polynomials -- Environmental modelling -- Ecological data -- Environmental data; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2020.104663
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This paper introduces a polynomial feedforward neural network based on Chebyshev polynomials able to effectively model non-linear and highly complex environmental data. The data sets were cautiously selected from the fields of biology, ecology, climate, and environmental management, and economics as to represent a scientifically meaningful and consistent corpus of disparate domains of intensive focus and interest in current ecological/environmental research, covering issues related to body growth/age, biomass production, energy efficiency/consumption, and ecology/geographic extension. The proposed network uses a number of layers to estimate the output in terms of a weighted sum of truncated Chebyshev series expansions applied to linear combinations of the input variables, and it is trained by the differential evolution algorithm. Its performance was compared to three neural networks. First, a polynomial feedforward network that uses Hermite polynomials as activation function in the hidden nodes; second, a radial basis function neural network; third, a Takagi-Sugeno-Kang neuro-fuzzy network. All the above networks were trained by evolutionary optimization algorithms. The comparison was carried out by standard criteria such as the root mean square error and the mean absolute error. Moreover, a non-parametric Kruskal-Wallis statistical test used to compare the median values of the root mean square errors between methods. The main experimental outcomes are: (a) the network's efficiency improves for higher polynomial orders, (b) the statistical analysis suggests that the proposed network appears to be very competitive to the other three networks. Highlights: Modelling environmental systems. Ecological and environmental data. Body growth/age, biomass production, energy efficiency/consumption, and ecology/geographic extension organizational levels. Feedforward neural networks. Chebyshev polynomial neural networks.