Title :
Vehicle Classification for Single Loop Detector with Neural Genetic Controller: A Design Approach
Author :
Bajaj, Preeti ; Sharma, Prashant ; Deshmukh, Amol
Author_Institution :
G.H.Raisoni Coll. of Eng., Nagpur
fDate :
Sept. 30 2007-Oct. 3 2007
Abstract :
Vehicle class is an important parameter in the process of road-traffic measurement. Currently, algorithm for inductive loop detector (ILD) uses back propagation neural network for vehicle classification. It has disadvantage of being stuck in local minima also more number of computations are required to find final weights of FFNN. This paper discusses a developed algorithm to find out the weights of neural network. The genetic algorithm is used for finding out the weights and applying those in neural network. In this approach number of computations is reduced with minimized errors as compared to conventional algorithm of neural network. The results found are highly satisfactory.
Keywords :
backpropagation; feedforward neural nets; genetic algorithms; neurocontrollers; road traffic; road vehicles; traffic control; FFNN; back propagation neural network; feedforward neural network; genetic algorithm; inductive loop detector; neural genetic controller; road-traffic measurement; single loop detector; vehicle classification; Artificial neural networks; Automotive engineering; Detectors; Frequency; Genetics; Intelligent transportation systems; Intelligent vehicles; Neural networks; Neurons; Vehicle detection; Genetic algorithm; Intelligent System design; Neural network; Vehicle Classification; hybrid controller;
Conference_Titel :
Intelligent Transportation Systems Conference, 2007. ITSC 2007. IEEE
Conference_Location :
Seattle, WA
Print_ISBN :
978-1-4244-1396-6
Electronic_ISBN :
978-1-4244-1396-6
DOI :
10.1109/ITSC.2007.4357781
