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Speed adaptation using Neuro fuzzy approach

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L
Lucky Amesar

This project aims to design a hybrid neuro-fuzzy controller for longitudinal control of an automotive system to reduce road accidents caused by human error. The controller uses a generic self-organizing fuzzy-neural network (GenSOFNN) with Yager's inference scheme. It takes speed and anticipation as inputs and controls throttle and brake outputs. The system was trained using a driving simulator log file and aims to anticipate road conditions to improve safety.

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PROJECT ON Intelligent Speed Adaptation Using Neuro-Fuzzy Controller GUIDED BY PROF. S S WANKHEDE Kshetrimayum Jevel Singh Lucky Amesar Nisha Kanoo Praful Kambe G.H.RAISONI COLLEGE OF ENGINEERING Department of Electronics & Telecommunication
OBJECTIVES This Project aims towards design of Hybrid Controller using Neuro- Fuzzy technique for Longitudinal controlling of Automotive System
[object Object],[object Object],Introduction
Why do we need controller in vehicles? ,[object Object],[object Object],[object Object],[object Object],[object Object],But the basic reason for all this is …….. “ Human error”
WHO report figure on Road accidents …..
PROBLEM ANALYSIS ,[object Object],[object Object],[object Object],HUMAN APPROACH Efficient in Decision making NEURAL NETWORKS FUZZY LOGIC
HYBRID CONTROLLER Neural Network + Fuzzy Logic Good for learning. Not good for human to interpret its internal representation. ,[object Object],[object Object],[object Object],Human reasoning scheme. Fuzzy rules and membership functions are subjective. ,[object Object],[object Object]
HYBRID CONTROLLER Cont…. Neural Network + Fuzzy Logic Good for learning. Not good for human to interpret its internal representation. ,[object Object],[object Object],[object Object],Human reasoning scheme. Fuzzy rules and membership functions are subjective. ,[object Object],[object Object],A Neuro-fuzzy system is a fuzzy system that uses a learning algorithm derived from or inspired by neural network theory to determine its parameters by processing data samples.
[object Object],[object Object],[object Object],[object Object],VEHICLE CONTROLLER ,[object Object]
Yager’s Inference Scheme ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
STRUCTURE OF GenSOFNN Layer 1 input linguistic nodes Layer 2 input term nodes Layer 3 rule nodes Layer 4 Output term node Layer 5 output linguistic nodes
Fuzzifier Inference Engine Defuzzifier STRUCTURE OF GenSOFNN contd.. Layer 1 input linguistic nodes Layer 2 input term nodes Layer 3 rule nodes Layer 4 Output term node Layer 5 output linguistic nodes
antecedent consquent STRUCTURE OF GenSOFNN contd… DIC Technique` is used Layer 1 input linguistic nodes Layer 2 input term nodes Layer 3 rule nodes Layer 4 Output term node Layer 5 output linguistic nodes
Adaptation to vehicle controller GenSoYager GenSoYager anticipation speed Throttle speed anticipation Brake A.) Implementation of Longitudinal Control
Adaptation to vehicle controller B.) Training of GenSoYager system Human Driver Driving Simulator Log File Visual Feedback Action Action
INPUTS TO VEHICLE CONTROLLER INPUTS Speed Anticipation ,[object Object],[object Object],1. It depends upon the curve & distance from curve. 2. Calculated by using log file.
Calculation of anticipation factor speed distance Anticipation algorithm anticipation
IMPLEMENTATION Problem analysis HUMAN approach Decision making GENSOFNN Training using Error back propagation algorithm & log file Provided with Fuzzy set of rules to interpret
ADVANTAGES ,[object Object],[object Object],[object Object]
DRAWBACKS ,[object Object],[object Object],[object Object]
Scope of Work ,[object Object]
References ,[object Object],[object Object],[object Object]
THANKING YOU QUESTIONS?

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Speed adaptation using Neuro fuzzy approach

  • 1. PROJECT ON Intelligent Speed Adaptation Using Neuro-Fuzzy Controller GUIDED BY PROF. S S WANKHEDE Kshetrimayum Jevel Singh Lucky Amesar Nisha Kanoo Praful Kambe G.H.RAISONI COLLEGE OF ENGINEERING Department of Electronics & Telecommunication
  • 2. OBJECTIVES This Project aims towards design of Hybrid Controller using Neuro- Fuzzy technique for Longitudinal controlling of Automotive System
  • 3.
  • 4.
  • 5. WHO report figure on Road accidents …..
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  • 11. STRUCTURE OF GenSOFNN Layer 1 input linguistic nodes Layer 2 input term nodes Layer 3 rule nodes Layer 4 Output term node Layer 5 output linguistic nodes
  • 12. Fuzzifier Inference Engine Defuzzifier STRUCTURE OF GenSOFNN contd.. Layer 1 input linguistic nodes Layer 2 input term nodes Layer 3 rule nodes Layer 4 Output term node Layer 5 output linguistic nodes
  • 13. antecedent consquent STRUCTURE OF GenSOFNN contd… DIC Technique` is used Layer 1 input linguistic nodes Layer 2 input term nodes Layer 3 rule nodes Layer 4 Output term node Layer 5 output linguistic nodes
  • 14. Adaptation to vehicle controller GenSoYager GenSoYager anticipation speed Throttle speed anticipation Brake A.) Implementation of Longitudinal Control
  • 15. Adaptation to vehicle controller B.) Training of GenSoYager system Human Driver Driving Simulator Log File Visual Feedback Action Action
  • 16.
  • 17. Calculation of anticipation factor speed distance Anticipation algorithm anticipation
  • 18. IMPLEMENTATION Problem analysis HUMAN approach Decision making GENSOFNN Training using Error back propagation algorithm & log file Provided with Fuzzy set of rules to interpret
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  • 22.