              TAHERE .ROYANI 1 ,JAVAD. HADDADNIA 2 , MOHAMMAD. ALIPOOR 3 (1,2,3) Department of electrical engineering Tarbiat Moallem University of Sabzevar Sabzevar Iran tahere.royani@gmail.com ; Haddania@sttu.ac.ir ; m.biomedical@yahoo.com  In this paper a fuzzy neural network is applied for real time traffic signal control at an isolated intersection. The FNN has advantages of both fuzzy expert system (fuzzy reasoning) and artificial neural network (self5study). A traffic light controller based on fuzzy neural network can be used for optimum control of fluctuating traffic volumes such as oversaturated or unusual load condition. The objective is to improve the vehicular throughput and minimize delays. The rules of fuzzy logic controller are formulated by following the same protocols that a human operator would use to control the time intervals of the traffic light. For adjusting the parameters of FNN, genetic algorithm was used. Compared with traditional control methods for traffic signal, the proposed FNN algorithm shows better performances and adaptability.  Fuzzy Neural Network, Traffic Control, Delay, Genetic Algorithm, Performance.   Traffic congestion is a crucial problem in large cities. Signal control methods include traditional control methods and intelligent control methods. Since intelligent control methods are superior to traditional control methods, lots of intelligent signal control models were put forward in recent years. It is partially caused by improper control of traffic lights, which is not corresponding to the current traffic conditions. To alleviate traffic congestion in urban areas, the concept of Intelligent Transportation Systems (ITS) has been widely accepted in developed countries. ITS is a highly promising system for providing key solutions to current road congestion problems [1]. The problem of intelligent traffic control has been studied in the area of ITS for many years. There are many conventional methods for traffic signal control but most of them sometimes fail to deal efficiently with the complex, time5varying traffic conditions and controller can’t satisfy real5time character for traffic signal [2]. They are modeled based on the preset cycle time to change the signal without any analysis of traffic situation. Due to fixed cycle time, such systems do not consider that which intersection has more load of traffic, so should kept green more or should terminate earlier then complete cycle time. Recently, major research on urban traffic focuses on artificial intelligence techniques, such as fuzzy control, genetic algorithm and neural network. Using timed Petri Nets [3], SPSA [4], ant algorithm [5], knowledge based multi5agent system [6, 7], and a mobile agent [8] have also been suggested. Trabia [9] designed a multi5phase fuzzy logic controller for an isolated intersection with through and left5turning movements. In [10], a new fuzzy controller based on fuzzy logic and weighting coefficients is designed. Bingham [11] obtained intersection fuzzy control parameters from neural networks, and improved fuzzy control result. Chen Xiangjun [12] put forward a self5 learning traffic signal control approach, which controls intersection signal with fuzzy algorithm, and updates fuzzy control rules with genetic algorithm. These studies have their own characteristics and theoretical foundations; however, an intersection signal control model should consider three factors: (1) Simplified computing model, control schemes should output in a specified period; (2) consider both under control intersection and its adjacent intersection, for realizing linear or group control; (3) self5learning ability. This paper tries to consider these factors in intersection control model. Through fuzzy classifying traffic flow in under control intersection, the model save signal control schemes in different traffic flow into knowledge5database as rule set. In control process, the model use neural network to update rule set according to different control effect of control schemes in different traffic flow, thus the model has self5learning ability [13]. The rules of fuzzy logic controller are formulated by following the same protocols that a human operator would use to control the time intervals of the traffic light. The length of current green phase is extended or terminated depending upon the “arrival”, number of vehicle approaching the green phase NEW ASPECTS of SIGNAL PROCESSING, COMPUTATIONAL GEOMETRY and ARTIFICIAL VISION ISSN: 1792-4618 87 ISBN: 978-960-474-217-2