Computational intelligence techniques and applications

Computational intelligence is a group of computational models and tools that encompass elements of learning, adaptation, and/or heuristic optimization. It is used to help study problems that are difficult to solve using conventional computational algorithms. Neural networks, evolutionary computation, and fuzzy systems are the three main pillars of computational intelligence. More recently, emerging areas such as swarm intelligence, artificial immune systems (AIS), support vector machines, rough sets, chaotic systems, and others have been added to the range of computational intelligence techniques. This chapter aims to present an overview of computational intelligence techniques and their applications, focusing on five representative techniques, including neural networks, evolutionary computation, fuzzy systems, swarm intelligence, and AIS.

[1]  Li Li,et al.  An Artificial Immune Approach for Vehicle Detection from High Resolution Space Imagery , 2007 .

[2]  Maria J. Diamantopoulou,et al.  Estimating Crimean juniper tree height using nonlinear regression and artificial neural network models , 2013 .

[3]  M. Watts,et al.  Determining factors that influence the dispersal of a pelagic species: A comparison between artificial neural networks and evolutionary algorithms , 2011 .

[4]  Kwok-wing Chau.  A split-step particle swarm optimization algorithm in river stage forecasting , 2007 .

[5]  Aristotelis Mantoglou,et al.  Management of coastal aquifers based on nonlinear optimization and evolutionary algorithms , 2004 .

[6]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[7]  Bernard De Baets,et al.  Single versus multiple objective genetic algorithms for solving the even-flow forest management problem , 2004 .

[8]  Sadan Kulturel-Konak,et al.  A review of clonal selection algorithm and its applications , 2011, Artificial Intelligence Review.

[9]  Lazaros S. Iliadis,et al.  An intelligent system employing an enhanced fuzzy c-means clustering model: Application in the case of forest fires , 2010 .

[10]  Belkacem Draoui,et al.  Optimization of Greenhouse Climate Model Parameters Using Particle Swarm Optimization and Genetic Algorithms , 2011 .

[11]  Davor Z Antanasijević,et al.  PM(10) emission forecasting using artificial neural networks and genetic algorithm input variable optimization. , 2013, The Science of the total environment.

[12]  Biswajeet Pradhan,et al.  Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS , 2012, Comput. Geosci..

[13]  Amit Konar,et al.  Computational Intelligence: Principles, Techniques and Applications , 2005 .

[14]  Ricard V. Solé,et al.  Macroevolutionary algorithms: a new optimization method on fitness landscapes , 1999, IEEE Trans. Evol. Comput..

[15]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

[16]  Sovan Lek,et al.  Artificial neural networks as a tool in ecological modelling, an introduction , 1999 .

[17]  Paul Helman,et al.  An immunological approach to change detection: algorithms, analysis and implications , 1996, Proceedings 1996 IEEE Symposium on Security and Privacy.

[18]  Feng Liu,et al.  Land-use spatial optimization based on PSO algorithm , 2011, Geo spatial Inf. Sci..

[19]  Constantin V. Negoita,et al.  On Fuzzy Systems , 1978 .

[20]  Beidou Xi,et al.  Using Artificial Neural Network Models for Eutrophication Prediction , 2013 .

[21]  Yingjie Yang,et al.  Artificial neural networks linked to GIS , 2003 .

[22]  A. K. Lohani,et al.  Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques , 2012 .

[23]  Keith L. Downing,et al.  Using evolutionary computational techniques in environmental modelling , 1998 .

[24]  Dan W. Patterson,et al.  Introduction to artificial intelligence and expert systems , 1990 .

[25]  Juan José González de la Rosa,et al.  A novel inference method for local wind conditions using genetic fuzzy systems , 2011 .

[26]  Edwin E. Herricks,et al.  A self-organizing radial basis network for estimating riverine fish diversity , 2013 .

[27]  Chidchanok Lursinsap,et al.  Integration of unsupervised and supervised neural networks to predict dissolved oxygen concentration in canals , 2013 .

[28]  Jean-François Mas,et al.  Mapping land use/cover in a tropical coastal area using satellite sensor data, GIS and artificial neural networks , 2004 .

[29]  Sheng Wang,et al.  Modeling Turbidity Intrusion Processes in Flooding Season of a Canyon-Shaped Reservoir, South China , 2012 .

[30]  Anne Auger,et al.  Theory of Randomized Search Heuristics: Foundations and Recent Developments , 2011, Theory of Randomized Search Heuristics.

[31]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[32]  Jae-Won Yoo,et al.  Effective prediction of biodiversity in tidal flat habitats using an artificial neural network. , 2013, Marine environmental research.

[33]  Donghee Park,et al.  System optimization for eco-design by using monetization of environmental impacts: a strategy to convert bi-objective to single-objective problems , 2013 .

[34]  Halil Karahan,et al.  Prediction of hard rock TBM penetration rate using particle swarm optimization , 2011 .

[35]  Jose C. Principe,et al.  Neural and adaptive systems : fundamentals through simulations , 2000 .

[36]  Andrew Kusiak,et al.  Optimization of wind turbine energy and power factor with an evolutionary computation algorithm , 2010 .

[37]  Young-Seuk Park,et al.  The application of Artificial Neural Network (ANN) model to the simulation of denitrification rates in mesocosm-scale wetlands , 2013, Ecol. Informatics.

[38]  Leandro Nunes de Castro,et al.  aiNet: An Artificial Immune Network for Data Analysis , 2002 .

[39]  Anne Auger,et al.  Theory of Evolution Strategies: A New Perspective , 2011, Theory of Randomized Search Heuristics.

[40]  Lin Qiu,et al.  Genetic Programming for Modelling Long-Term Hydrological Time Series , 2009, 2009 Fifth International Conference on Natural Computation.

[41]  Jasna Radulović,et al.  Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia , 2010 .

[42]  B. Fisher.  Fuzzy environmental decision-making: applications to air pollution , 2003 .

[43]  Lei Zhang,et al.  A resource limited artificial immune system algorithm for supervised classification of multi/hyper‐spectral remote sensing imagery , 2007 .

[44]  Xavier Bonnaire,et al.  EA-MP: An evolutionary algorithm for a mine planning problem , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[45]  Chien-Ming Chou.  Particle Swarm Optimization for Identifying Rainfall-Runoff Relationships , 2012 .

[46]  Jesús Ariel Carrasco-Ochoa,et al.  Assessment and prediction of air quality using fuzzy logic and autoregressive models , 2012 .

[47]  Jungho Im,et al.  An artificial immune network approach to multi-sensor land use/land cover classification , 2011 .

[48]  César Hervás-Martínez,et al.  A multi-objective neural network based method for cover crop identification from remote sensed data , 2012, Expert Syst. Appl..

[49]  Xinjie Yu,et al.  Introduction to evolutionary algorithms , 2010, The 40th International Conference on Computers & Indutrial Engineering.

[50]  B. Muys,et al.  Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests , 2010 .

[51]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[52]  D. Torkar,et al.  Application of artificial neural networks in simulating radon levels in soil gas , 2010 .

[53]  Christian Blum,et al.  Swarm Intelligence: Introduction and Applications , 2008, Swarm Intelligence.

[54]  Bhoop Singh,et al.  Artificial neural network model as a potential alternative for groundwater salinity forecasting , 2011 .

[55]  D. Dasgupta.  Artificial Immune Systems and Their Applications , 1998, Springer Berlin Heidelberg.

[56]  C. L. Wu,et al.  Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis , 2011 .

[57]  Vladimir M. Krasnopolsky,et al.  The Application of Neural Networks in the Earth System Sciences: Neural Networks Emulations for Complex Multidimensional Mappings , 2013 .

[58]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[59]  Michael J. Watts,et al.  A novel method for mapping reefs and subtidal rocky habitats using artificial neural networks , 2011 .

[60]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[61]  A. Ahmadi,et al.  Daily suspended sediment load prediction using artificial neural networks and support vector machines , 2013 .

[62]  Mohammad Ali Riahi,et al.  Permeability prediction and construction of 3D geological model: application of neural networks and stochastic approaches in an Iranian gas reservoir , 2012, Neural Computing and Applications.

[63]  WenJun Zhang,et al.  Computational Ecology: Artificial Neural Networks and Their Applications , 2010 .

[64]  M. H. Afshar,et al.  A parameter free Continuous Ant Colony Optimization Algorithm for the optimal design of storm sewer networks: Constrained and unconstrained approach , 2010, Adv. Eng. Softw..

[65]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[66]  Shenglian Guo,et al.  A macro-evolutionary multi-objective immune algorithm with application to optimal allocation of water resources in Dongjiang River basins, South China , 2012, Stochastic Environmental Research and Risk Assessment.

[67]  Ahmad Taher,et al.  Adaptive Neuro-Fuzzy Systems , 2010 .

[68]  P. Kayastha.  Application of fuzzy logic approach for landslide susceptibility mapping in Garuwa sub-basin, East Nepal , 2012, Frontiers of Earth Science.

[69]  Adam P. Piotrowski,et al.  Optimizing neural networks for river flow forecasting – Evolutionary Computation methods versus the Levenberg–Marquardt approach , 2011 .

[70]  Martin T. Hagan,et al.  Neural network design , 1995 .

[71]  Mehmet Özger,et al.  Prediction of ocean wave energy from meteorological variables by fuzzy logic modeling , 2011, Expert Syst. Appl..

[72]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[73]  A. St‐Hilaire,et al.  Assessment of Atlantic salmon (Salmo salar) habitat quality and its uncertainty using a multiple-expert fuzzy model applied to the Romaine River (Canada) , 2013 .

[74]  S. I. V. Sousa,et al.  Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations , 2007, Environ. Model. Softw..

[75]  Takanori Shibata,et al.  Genetic Algorithms And Fuzzy Logic Systems Soft Computing Perspectives , 1997 .

[76]  M. Gardner,et al.  Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London , 1999 .

[77]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[78]  C. Akbulut,et al.  Assessment of the impact of anthropogenic activities on the groundwater hydrology and chemistry in Tarsus coastal plain (Mersin, SE Turkey) using fuzzy clustering, multivariate statistics and GIS techniques , 2012 .

[79]  Dipankar Dasgupta,et al.  An Anomaly Entection Algorithm Inspired by the Immune Syste , 1999 .

[80]  U. Okkan,et al.  Wavelet neural network model for reservoir inflow prediction , 2012 .

[81]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[82]  H. Alizadeh,et al.  Coupled stochastic soil moisture simulation‐optimization model of deficit irrigation , 2013 .

[83]  B. Monger,et al.  Improving ecological forecasts of copepod community dynamics using genetic algorithms , 2010 .

[84]  YuHai Bao,et al.  Wetland Landscape Classification Based on the BP Neural Network in DaLinor Lake Area , 2011 .

[85]  A. Klein,et al.  Fractional snow cover mapping through artificial neural network analysis of MODIS surface reflectance , 2011 .

[86]  A. Ahmadi,et al.  Groundwater Vulnerability Assessment Using Fuzzy Logic: A Case Study in the Zayandehrood Aquifers, Iran , 2012, Environmental Management.

[87]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[88]  Andries P. Engelbrecht,et al.  Computational Intelligence: An Introduction , 2002 .

[89]  Jan-Tai Kuo,et al.  USING ARTIFICIAL NEURAL NETWORK FOR RESERVOIR EUTROPHICATION PREDICTION , 2007 .

[90]  Riccardo Poli,et al.  Foundations of Genetic Programming , 1999, Springer Berlin Heidelberg.