Conjugate Gradient Trained Neural Network for Intelligent Sensing of Manhole Gases to Avoid Human Fatality

Computational Intelligence offers solution to various real life problems. Artificial Neural Network (ANN) has the capability of solving highly complex and nonlinear problems. The present chapter demonstrates the application of these tools to provide solutions to the manhole gas detection problem. Manhole, the access point across sewer pipeline system, contains various toxic and explosive gases. Hence, predetermination of these gases before accessing manholes is becoming imperative. The problem is treated as a pattern recognition problem. ANN, devised for solving this problem, is trained using a supervised learning algorithm. The conjugate gradient method is used as an alternative of back propagation neural network learning algorithm for training of the ANN. The chapter offers comprehensive performance analysis of the learning algorithm used for the training of ANN followed by discussion on the methods of presenting the system result. The authors discuss different variants of Conjugate Gradient and propose two new variants of it. Paramartha Dutta Visva-Bharati University, India Varun Kumar Ojha Visva-Bharati University, India

[1]  D. G. Sotiropoulos,et al.  A SPECTRAL VERSION OF PERRY'S CONJUGATE GRADIENT METHOD FOR NEURAL NETWORK TRAINING , 2002 .

[2]  C. Storey,et al.  Efficient generalized conjugate gradient algorithms, part 1: Theory , 1991 .

[3]  Varun Kumar Ojha,et al.  Performance Comparison of Different Intelligent Techniques Applied on Detecting Proportion of Different Component in Manhole Gas Mixture , 2013 .

[4]  Jadranka Skorin-Kapov,et al.  Training Artificial Neural Networks: Backpropagation via Nonlinear Optimization , 2001 .

[5]  P. Wolfe.  Convergence Conditions for Ascent Methods. II: Some Corrections , 1971 .

[6]  Adisorn Tuantranont,et al.  Portable electronic nose based on carbon nanotube-SnO2 gas sensors and its application for detection of methanol contamination in whiskeys , 2010 .

[7]  P. Wolfe.  Convergence Conditions for Ascent Methods. II , 1969 .

[8]  Minho Lee,et al.  Micro gas sensor array with neural network for recognizing combustible leakage gases , 2005 .

[9]  Ya-Xiang Yuan,et al.  A Nonlinear Conjugate Gradient Method with a Strong Global Convergence Property , 1999, SIAM J. Optim..

[10]  Parvinder S. Sandhu,et al.  A Comparative Analysis of Conjugate Gradient Algorithms & PSO Based Neural Network Approaches for Reusability Evaluation of Procedure Based Software Systems , 2011 .

[11]  Tim Struttmann,et al.  Unintentional Carbon Monoxide Poisoning From an Unlikely Source , 1998, The Journal of the American Board of Family Medicine.

[12]  L. Armijo.  Minimization of functions having Lipschitz continuous first partial derivatives. , 1966 .

[13]  En Sup Yoon,et al.  The Estimation of Hazardous Gas Release Rate Using Optical Sensor and Neural Network , 2010 .

[14]  E. Llobet,et al.  Multicomponent gas mixture analysis using a single tin oxide sensor and dynamic pattern recognition , 2001, IEEE Sensors Journal.

[15]  Varun Kumar Ojha,et al.  Performance Analysis Of Neuro Genetic Algorithm Applied On Detecting Proportion Of Components In Manhole Gas Mixture , 2012, ArXiv.

[16]  Jun Li.  A mixed gas sensor system based on thin film SAW sensor array and neural network , 1993, [1993] Proceedings of the Twelfth Southern Biomedical Engineering Conference.

[17]  C. Charalambous,et al.  Conjugate gradient algorithm for efficient training of artifi-cial neural networks , 1990 .

[18]  Guoyin Li,et al.  New conjugacy condition and related new conjugate gradient methods for unconstrained optimization , 2007 .

[19]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[20]  Varun Kumar Ojha,et al.  Application of Real Valued Neuro Genetic Algorithm in Detection of Components Present in Manhole Gas Mixture , 2012 .

[21]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[22]  C. M. Reeves,et al.  Function minimization by conjugate gradients , 1964, Comput. J..

[23]  V. K. Ojha,et al.  A Novel Neuro Simulated Annealing Algorithm for Detecting Proportion of Component Gases in Manhole Gas Mixture , 2012, 2012 International Conference on Advances in Computing and Communications.

[24]  Xiaodong Zhang.  A conjugate gradient-neural network technique for ultrasound inverse imaging , 2002 .

[25]  Varun Kumar Ojha,et al.  Linear regression based statistical approach for detecting proportion of component gases in manhole gas mixture , 2012, 2012 1st International Symposium on Physics and Technology of Sensors (ISPTS-1).

[26]  Zhou Tao,et al.  Mixed Gases Recognition Based on Feedforward Neural Network , 2009 .

[27]  Avinoam Perry,et al.  Technical Note - A Modified Conjugate Gradient Algorithm , 1978, Oper. Res..

[28]  Hakim Baha,et al.  A Novel Neural Network-Based Technique for Smart Gas Sensors Operating in a Dynamic Environment , 2009, Sensors.

[29]  Kaushal K. Shukla,et al.  On the design issue of intelligent electronic nose system , 2000, Proceedings of IEEE International Conference on Industrial Technology 2000 (IEEE Cat. No.00TH8482).

[30]  A. Perry.  A Modified Conjugate Gradient Algorithm for Unconstrained Nonlinear Optimization , 1975 .

[31]  Varun Kumar Ojha,et al.  Performance analysis of neuro swarm optimization algorithm applied on detecting proportion of components in manhole gas mixture , 2012, Artif. Intell. Res..

[32]  Yong Zhang,et al.  Cross sensitivity reduction of gas sensors using genetic algorithm neural network , 2001, SPIE Optics East.

[33]  J. Borwein,et al.  Two-Point Step Size Gradient Methods , 1988 .

[34]  Amine Bermak,et al.  A Spiking Neural Network for Gas Discrimination Using a Tin Oxide Sensor Array , 2008, 4th IEEE International Symposium on Electronic Design, Test and Applications (delta 2008).

[35]  M. Hestenes,et al.  Methods of conjugate gradients for solving linear systems , 1952 .

[36]  Mark Goldstein,et al.  Carbon monoxide poisoning. , 2008, Journal of emergency nursing: JEN : official publication of the Emergency Department Nurses Association.

[37]  J. M. Martínez,et al.  A Spectral Conjugate Gradient Method for Unconstrained Optimization , 2001 .

[38]  Wu Pan,et al.  Application of electronic nose in gas mixture quantitative detection , 2009, 2009 IEEE International Conference on Network Infrastructure and Digital Content.

[39]  Kaoru Hirota,et al.  A fuzzy-similarity-based self-organized network inspired by immune algorithm for three-mixture-fragrance recognition , 2006, IEEE Transactions on Industrial Electronics.