Applying intelligent systems in industry: A realistic overview

The objective of this paper is to give a realistic overview of the current state of the art of intelligent systems in industry based on the experience from applying these systems in a large global corporation. It includes a short analysis of the differences between academic and industrial research, examples of the key implementation areas of intelligent systems in manufacturing and business, a discussion about the main factors for success and failure of industrial intelligent systems, an estimate of the projected industrial needs that may drive future applications of intelligent systems, and some ideas how to improve academic-industrial collaboration in this research area.

[1]  Arthur K. Kordon,et al.  Variable Selection in Industrial Datasets Using Pareto Genetic Programming , 2006 .

[2]  Christiaan Aldrich,et al.  Development of neurocontrollers with evolutionary reinforcement learning , 2005, Comput. Chem. Eng..

[3]  Arthur K. Kordon,et al.  Hybrid intelligent systems for industrial data analysis , 2002, Proceedings First International IEEE Symposium Intelligent Systems.

[4]  Lotfi A. Zadeh,et al.  Fuzzy logic = computing with words , 1996, IEEE Trans. Fuzzy Syst..

[5]  Bogdan Gabrys,et al.  Data-driven Soft Sensors in the process industry , 2009, Comput. Chem. Eng..

[6]  Yun Li,et al.  Patents, software, and hardware for PID control: an overview and analysis of the current art , 2006, IEEE Control Systems.

[7]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[8]  Joachim von Puttkamer.  Editors , 2018, Journal of Modern European History.

[9]  Ying L. Becker,et al.  Stock Selection - an Innovative Application of Genetic Programming Methodology , 2006 .

[10]  Yun Li,et al.  PID Control : Patents, software, and hardware for PID control , 2006 .

[11]  Arthur K. Kordon,et al.  Applying Computational Intelligence - How to Create Value , 2009 .

[12]  Arthur K. Kordon,et al.  Biomass Inferential Sensor Based on Ensemble of Models Generated by Genetic Programming , 2004, GECCO.

[13]  Arthur K. Kordon,et al.  Hybrid Genetic Programming−First-Principles Approach To Process and Product Modeling , 2010 .

[14]  Arthur K. Kordon,et al.  Competitive advantages of evolutionary computation for industrial applications , 2005, 2005 IEEE Congress on Evolutionary Computation.

[15]  David G. Schwartz,et al.  Concurrent marketing analysis: a multi‐agent model for product, price, place and promotion , 2000 .

[16]  Michael P. Clements,et al.  Forecasting economic and financial time-series with non-linear models , 2004 .

[17]  S. Griffis.  EDITOR , 1997, Journal of Navigation.

[18]  Thomas H. Davenport,et al.  Analytics at Work: Smarter Decisions, Better Results , 2010 .

[19]  Dimitar Filev,et al.  Intelligent systems in the automotive industry: applications and trends , 2007, Knowledge and Information Systems.

[20]  Arthur K. Kordon,et al.  Robust Inferential Sensors Based on Ensemble of Predictors Generated by Genetic Programming , 2004, PPSN.

[21]  Plamen Angelov,et al.  Evolving Inferential Sensors in the Chemical Process Industry , 2010 .

[22]  Piero P. Bonissone,et al.  Hybrid soft computing systems: industrial and commercial applications , 1999, Proc. IEEE.

[23]  A. Brabazon,et al.  An Introduction to Evolutionary Computation in Finance , 2008, IEEE Computational Intelligence Magazine.

[24]  Arthur K. Kordon,et al.  Robust soft sensor development using genetic programming , 2003 .

[25]  Chip Wells,et al.  Applied Data Mining for Forecasting Using SAS , 2012 .

[26]  James Norman Cawse,et al.  Experimental Design for Combinatorial and High Throughput Materials Development , 2002 .

[27]  Arthur K. Kordon,et al.  Robust soft sensors based on integration of genetic programming, analytical neural networks, and support vector machines , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[28]  Luigi Fortuna,et al.  Soft Sensors for Monitoring and Control of Industrial Processes (Advances in Industrial Control) , 2006 .

[29]  Arthur K. Kordon,et al.  Soft sensor development using genetic programming , 2001 .

[30]  Guido Smits,et al.  Hybrid model development methodology for industrial soft sensors , 2003, Proceedings of the 2003 American Control Conference, 2003..

[31]  Mark Kotanchek,et al.  Pareto-Front Exploitation in Symbolic Regression , 2005 .