1 / 10

Core Group: Soft Computing Techniques and Application

Core Group: Soft Computing Techniques and Application. Fuzzy Logic Artificial Neural Network Genetic Algorithms & Evolution Prog. Hybrid Models. Core Members. Prof Rekha Govil Dr. Neeta Khare Dr. Ritu Vijay Ms. Kusum Gupta Mrs. Pratishtha Mathur Ms. Archana Mangal

sai
Download Presentation

Core Group: Soft Computing Techniques and Application

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Core Group: Soft Computing Techniques and Application Fuzzy Logic Artificial Neural Network Genetic Algorithms & Evolution Prog. Hybrid Models

  2. Core Members • Prof Rekha Govil • Dr. Neeta Khare • Dr. Ritu Vijay • Ms. Kusum Gupta • Mrs. Pratishtha Mathur • Ms. Archana Mangal • Mr. Anurag Singh Baghel

  3. Work Area Broad Objectives: Mapping, Process & nonlinear Behaviour modeling, Optimum searching in various Applications Smart Instrumentation Signal Processing Process Control Image Processing Achievements: International & National Publications/ presentation Patent

  4. Research Projects of Core Group

  5. Research Work Smart Instrumentation:Intelligent Battery Monitoring, Doctoral Work by Neeta Khare Battery’s Nonlinear Behaviour Modeling using Neuro Fuzzy and regression technique to indicate Online State of Charge (SoC) and State of Health (SoH) Future Plans: • Addition of more parameters like Corrosion rate, online temperature, fixed parameters of battery • Generalization of model through multiple battery application • Neuro Fuzzy implementation for SoH • Fuzzy Rule implementation learned through NN • Genetic Algorithm modeling for Battery behaviour • Hybrid Modeling (using soft computing) for Battery • VLSI implementation of model for Embedded system On the work a Process patent has been obtained: 813/KOL/ 2005 Also Another Project in pipeline with NSTL (Naval Science &Technology Laboratory)

  6. Signal Processing: Neural Network Application Doctoral work by Ritu Vijay Performance Evaluation of Radial Basis Function on Signal representation, compression, interpolation and extrapolation capabilities. Future Plans: VLSI Implementation of ANN Prediction Tool for Tidal Height Indication Noise Analysis through RBF & Noise Filtering

  7. Image Processing: Dynamic Modeling for pattern recognition using Neural Network Doctoral work (In progress) by Pratishtha Mathur Capability analysis of ANN for Pattern recognition for Satellite Images (temporal and spatial Changes) Future Plan: Hardware Implementation Model for fore cast Forecast tool development Neuro- fuzzy Modeling Performance Analysis of hybrid Model

  8. Supervised Unsupervised Image Processing: Trend Analysis in satellite imagery using Temporal Kohonen Map Doctoral Work (in progress) by Archana Mangal Dynamic Modeling technique to detect short and long term trend by analyzing the changes in spatial and temporal features of satellite images. Work Done: Classification of Image, Analysis: Monthly Changes, Yearly Changes Future Plan Designing Algorithm using Temporal Kohenen Map Designing an Algorithm for Hierarchical SOFM Comparison: performance of algorithms Testing the algorithm on Satellite images A project in pipeline to be submitted to DST in collaboration with Remote Sensing group, BISR, Jaipur

  9. Process Control: A Generalized Neuro-Fuzzy Controller Design Doctoral work(in progress) By Kusum Gupta This work aims at the design and development of a generalized Neuro-Fuzzy controller where in for any control problem the controller is first trained offline with the plant data and then it provides user interfaces and data structures to input the plant parameters, control conditions and then results in online generation of fine tuned value of the control parameter Future Plan: This approach results into more efficient, hardware implement- able controller design methodology.

  10. FPGA Based Analog Signaling Mixed Signaling Fuzzy- ANN ANN-GA Fuzzy- GA Biomedical Application Intelligent Instrumentation Defense Application Fault Tolerance System Critical application area Common Target: Core Group • VLSI Implementation Dedicated Processor Design Performance Evaluation of various algorithms and hybrid models • New Application Area • Generic Model Design

More Related