Identification of Vehicle Class and Speed for Mixed Sensor Technology using Fuzzy- Neural & Genetic Algorithm : A Design Approach

Vehicle classification is an important parameter in Road Traffic Management System. The paper includes vehicle classification using signals from different sensors, process and mix those signals, to extract features helpful to identify vehicle class and also its speed. The important features needed are axle distance, length of vehicle, height of chassis and occupancy time. These features are then fed as input to fuzzy-neural-genetic hybrid controller which process the information and generates two output -i) vehicle class & ii) speed. This paper simulates the output using only fuzzy logic controller. It then uses fuzzyneural approach to see the improvement in output. Finally genetic algorithm is used to optimize the fuzzy neural controller so that accurate class and speed are identified in relatively lesser iterations. Further if member ship function of fuzzy inputs are itself vague i.e. fuzzy then instead of type-1 fuzzy logic, type-2 fuzzy logic is implemented and the entire system is to be simulated again for improving the efficiency of the controller. A research approach abstraction using type-2 fuzzy logic, neural and genetic algorithm is presented.