Dayalbagh Educational Institute Soft Computing Edge Cutting Technology Lab (DEISEL)
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1 Dayalbagh Educational Institute From the SelectedWorks of D. K. Chaturvedi Dr. Winter December 26, 2010 Dayalbagh Educational Institute Soft Computing Edge Cutting Technology Lab (DEISEL) D. K. Chaturvedi, Dayalbagh Educational Institute Available at:
2 (DEISEL) Soft Computing Group Professor Incharge 4 Teaching Staff members 4 Non-Teaching Staff Members 5 Ph.D. Students 6 M. Tech. Students Aim To exploit the tolerance for imprecision uncertainty, approximate reasoning and partial truth to achieve tractability, robustness, low solution cost, and close resemblance with human like decision making To find an approximate solution to an imprecisely/precisely formulated problem. Challenge The challenge is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. This, in essence, is the guiding principle of soft computing. Work done Published Books - 5 Journal Papers 46 Conference Papers - 89 Reveiwed Int. J. Papers 45
3 (DEISEL) Ph.D. Supporting staff Christian- Albrechts- Universität zu Kiel ADRDE Lab Agra
4 A. Variations in simple ANN 1. Simple feed-forward back-propagation ANN with sum neurons c - codes (ANN_sigma.c) 2. Feed-forward back-propagation ANN with Product neurons c - codes (ANN_pi.c) 3. Feed-forward back-propagation ANN with Sigma at hidden layer Product at output layer neurons c - codes (ANN_sigma_pi.c) 4. Feed-forward back-propagation ANN with Product at hidden layer Sigma at output layer neurons c - codes (ANN _pi_sigma.c) 5. Feed-forward back-propagation ANN with Product neurons with Gaussian Threshold function c - codes (ANN_pi_gaus.c) 6. Feed-forward back-propagation ANN with Product neurons with tangent hyperbolic Threshold function c - codes (ANN_pi_tanh.c) 7. Feed-forward back-propagation ANN with summation neurons with Gaussian function at hidden and linear at output c - codes (ANN_sigma_gaus_lin.c) 8. Feed-forward back-propagation ANN with summation neurons with sigmoidal function at hidden and linear at output c - codes (ANN_sigma_sig_lin.c) 9. Feed-forward back-propagation ANN with summation neurons with tanh function at hidden and linear at output c - codes (ANN_sigma_tanh_lin.c) 10. Feed-forward back-propagation ANN with summation neurons with guassian function c - codes (ANN_sigma_gaus.c) B. Development of Generlaized Neuron and its variations 1. Original Implementation: Generalized Neuron (GN) model c-code (GN.c) 2. GN Variations: i. GN Model0 (GN_model0.c) output = osigma*wsigma + opi*wpi ii. GN Model1 (GN_model1.c) output = osigma*wsigma + opi * (1-wsigma) iii. GN Model2 (GN_model2.c) output = (Osigma^wsigma)* (opi ^ (1-wsigma)) iv. GN Model3 (GN_model3.c) output = osigma * wsigma + (osigma+opi-osigma*opi)*(1- wsigma) v. GN Model4 (GN_model4.c) output = (osigma ^ wsigma) * ((osigma+opiosigma*opi)^(1-wsigma)) vi. GN Model5 (GN_model5.c) output = sqrt(osigma*opi)*wsigma + (osigma+opi)*(1- wsigma)/2.0 vii. GN Model6 (GN_model6.c)
5 output = sqrt(osigma*opi)^(1- wsigma)* (osigma+opi)/2.0) ^ (1- wsigma) viii. GN Model7 (GN_model7.c) 3. Revision: Generlaized Matlab ver. 6.5 Code GN1.m 4. Benchmark Testing: Benchmark.m i. Parity Problem ii. Voting Problem iii. Spiral Problem iv. Mackey Glass Problem 4. GN Applications in i. Machine Modeling ii. Power System Control iii. Electrical Load Forecasting iv. Channel Equalization v. Robotic Control vi. Air craft Landing Control C. Development of Fuzzy Expert Systems 1. Original Implementation: Fuzzy System model c-code (FS.c) 2. Variations: i. Application of different Aggregation Operators ii. Application of different Composition Operators iii. Application of different Implication Operators 3. FS Application in i. Machine Modeling ii. Load Forecasting iii. HIV/AIDS Population forecasting Problem D. Development of Evolutionary Algorithms 1. Original Implementation: i. Simple Genetic Algorithm (SGA.m) ii. Modified GA-Fuzzy Systems matlab (GAF.m) 2. EA Applications: i. Load Forecasting ii. Optimal Power Flow Problem iii. Transmission Pricing iv. Congestion management E. Integration of ANN, FS and EA i. Original Implementation: fuzzy Aggregated GN learning using GA+FS integrated Approach Matlab Codes. F. Quantum Neuron
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