Hierarchical fuzzy logic traffic control at a road junction using genetic algorithms

Heung, T.H.; Ho, Т.K.

doi:10.1109/fuzzy.1998.686284

Cited by 8 publications

(8 citation statements)

References 3 publications

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“…Plamena et al [43] designed an HFL model for natural risk assessment in Southwest Bulgaria. Heung et al [44] suggested a technique based on HFL and the genetic algorithm (GA) to control traffic at a road junction. HFL logic is designed to decrease the number of rules, and the GA algorithm is used for rule generation.…”

Section: Related Workmentioning

confidence: 99%

Underground Risk Index Assessment and Prediction Using a Simplified Hierarchical Fuzzy Logic Model and Kalman Filter

2018

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Normally, most of the accidents that occur in underground facilities are not instantaneous; rather, hazards build up gradually behind the scenes and are invisible due to the inherent structure of these facilities. An efficient inference system is highly desirable to monitor these facilities to avoid such accidents beforehand. A fuzzy inference system is a significant risk assessment method, but there are three critical challenges associated with fuzzy inference-based systems, i.e., rules determination, membership functions (MFs) distribution determination, and rules reduction to deal with the problem of dimensionality. In this paper, a simplified hierarchical fuzzy logic (SHFL) model has been suggested to assess underground risk while addressing the associated challenges. For rule determination, two new rule-designing and determination methods are introduced, namely average rules-based (ARB) and max rules-based (MRB). To determine efficient membership functions (MFs), a module named the heuristic-based membership functions allocation (HBMFA) module has been added to the conventional Mamdani fuzzy logic method. For rule reduction, a hierarchical fuzzy logic model with a distinct configuration has been proposed. In the simplified hierarchical fuzzy logic (SHFL) model, we have also tried to minimize rules as well as the number of levels of the hierarchical structure fuzzy logic model. After risk index assessment, the risk index prediction is carried out using a Kalman filter. The prediction of the risk index is significant because it could help caretakers to take preventive measures in time and prevent underground accidents. The results indicate that the suggested technique is an excellent choice for risk index assessment and prediction.

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Section: Related Workmentioning

confidence: 99%

Underground Risk Index Assessment and Prediction Using a Simplified Hierarchical Fuzzy Logic Model and Kalman Filter

2018

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“…4. Applying the equivalence of Kirchhoff's voltage law to the circuit in this figure, we can obtain (25) supplied voltage to the system; effective resistance of the magnetic circuit; effective inductance of the magnetic circuit; effective flux density of the magnetic circuit. Hence, we have a simplified dynamic model for the magnetic bearing system.…”

Section: Design Of the Active Magnetic Bearing Systemmentioning

confidence: 99%

“…(37) Notice that, since the real AMB system was built based on the mathematical model derived in Section II, where some key mechanical parameters were chosen through GA, and the final design successfully passed the FEM (finite element method) checking, the derived mathematical model should have captured the dynamical characteristics of the real system precisely. However, due to the measurement errors of some system parameters [e.g., the parameters in (37)] and the discretization error resulting from transforming a continuous system [(24) and (25)] to a discrete-time system [(34)-(36)] in digital simulations, (34)- (36) can only form a mathematical approximation of the real AMB system. Our testing in [42] showed that (34)-(36) and the real system have quite similar system responses (e.g., response shop, time constant) but differ in response details (e.g., overshoot value, ripples).…”

Section: Control Of the Magnetic Bearing Systemmentioning

confidence: 99%

“…Because GA's do not require or use derivative information, the most appropriate applications are problems where gradient information is unavailable or costly to obtain, such as reinforcement learning problems. GA's have been widely used for the design of fuzzy or neural controllers [25]- [30]. Karr [31] used GA's to alter the shape of the fuzzy sets used in a given rule base.…”

Section: Introductionmentioning

confidence: 99%

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GA-based fuzzy reinforcement learning for control of a magnetic bearing system

Lin

Jou

2000

IEEE Trans. Syst., Man, Cybern. B

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This paper proposes a TD (temporal difference) and GA (genetic algorithm)-based reinforcement (TDGAR) learning method and applies it to the control of a real magnetic bearing system. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to perform the reinforcement learning task. The TDGAR learning system is composed of two integrated feedforward networks. One neural network acts as a critic network to guide the learning of the other network (the action network) which determines the outputs (actions) of the TDGAR learning system. The action network can be a normal neural network or a neural fuzzy network. Using the TD prediction method, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network uses the GA to adapt itself according to the internal reinforcement signal. The key concept of the TDGAR learning scheme is to formulate the internal reinforcement signal as the fitness function for the GA such that the GA can evaluate the candidate solutions (chromosomes) regularly, even during periods without external feedback from the environment. This enables the GA to proceed to new generations regularly without waiting for the arrival of the external reinforcement signal. This can usually accelerate the GA learning since a reinforcement signal may only be available at a time long after a sequence of actions has occurred in the reinforcement learning problem. The proposed TDGAR learning system has been used to control an active magnetic bearing (AMB) system in practice. A systematic design procedure is developed to achieve successful integration of all the subsystems including magnetic suspension, mechanical structure, and controller training. The results show that the TDGAR learning scheme can successfully find a neural controller or a neural fuzzy controller for a self-designed magnetic bearing system.

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“…Pappis and Mamdani [2] designed a simple fuzzy logic controller to control traffic signals an isolated intersection of two one-way streets; Heung and Ho [3] used a hierarchical fuzzy logic controller with genetic algorithms to formulate the control rules; Trabia et al [4] designed a two-stage fuzzy logic controller for single traffic intersection signal control; Bingham [5] proposed a neurofuzzy traffic signal controller which used reinforcement learning in tuning the membership functions. All the research mentioned above have all reported better performance of the fuzzy logic controllers compared to a fixed-time controller or an actuated controller.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Logic Controller with Adaptive Dynamic Programming Optimization for Traffic Signals

Tian

Zhao

2008

2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery

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This paper proposes a fuzzy logic signal controller with adaptive dynamic programming optimizing for traffic intersection. Because fuzzy logic has a clear advantage that it is able to use expert knowledge well, we adopt it in our controller. As adaptive dynamic programming is an advanced technology which is suitable for solving nonlinear stochastic system optimizing problems, we use it to optimize our fuzzy logic controller. We set up an isolated intersection model of two-way roads with both left and right turns, and test the controller with ADP optimization under both constant and variable traffic flow rates. The simulation results show that this controller has a good performance.

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Hierarchical fuzzy logic traffic control at a road junction using genetic algorithms

Cited by 8 publications

References 3 publications

Underground Risk Index Assessment and Prediction Using a Simplified Hierarchical Fuzzy Logic Model and Kalman Filter

Underground Risk Index Assessment and Prediction Using a Simplified Hierarchical Fuzzy Logic Model and Kalman Filter

GA-based fuzzy reinforcement learning for control of a magnetic bearing system

A Fuzzy Logic Controller with Adaptive Dynamic Programming Optimization for Traffic Signals

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