F. Shabaninia

Shiraz University

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M. Vaziri

California State University, Sacramento

S. Vadhva

California State University, Sacramento

Reza Khorshidi

Shiraz University

Abolfazl Jalilvand

University of Zanjan

Mehdi Fatemi

Microsoft Research Montréal (Canada)

Sohrab Khanmohammadi

University of Tabriz

Mehdi Roopaei

University of Wisconsin–Platteville

S. Vadhava

California State University, Sacramento

Afrooz Ebadat

KTH Royal Institute of Technology

Mahdi Amini

Isfahan University of Technology

Cooperative Institutions

Shiraz University

California State University, Sacramento

Isfahan University of Technology

K.N.Toosi University of Technology

Islamic Azad University, Tehran

Islamic Azad University, Science and Research Branch

University of Tehran

University of Tabriz

California State University System

University of California, Berkeley

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Neural network algorithms for tuning of fuzzy certainty factor expert systems

Seyed Ali Jafari F. Shabaninia Patricia Nava

This paper presents a description of an algorithm that is used for tuning fuzzy certainty factors in a fuzzy expert system. Many expert system building tools have been developed, but only a few can produce systems that deal with inexact knowledge. One of the models that can deal with inexact knowledge is the fuzzy certainty factor expert system. Given a fuzzy certainty factor expert system, one of the very difficult tasks in its development is the tuning of the fuzzy certainty factors. This paper presents a method for using backpropagation, a well-known neural network training algorithm, for tuning of certainty factors. The fuzzy certainty factor expert system is defined, and then it is tuned by translating or mapping the fuzzy logic system to a feedforward neural network framework. The tuning then takes place as a session analogous to neural network training. This method is shown to be much more efficient than previous tuning methods. The application area of dental diagnostics is used to demonstrate the method. The system is reviewed and results that show its efficacy are discussed.

Certainty

Backpropagation

10.1109/wac.2002.1049527

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Citations (12)

Type-2 fuzzy multiagent traffic signal control

Bahareh Sabetghadam F. Shabaninia M. Vaziri S. Vadhava

This paper proposes a controller design for urban traffic networks. The growing demand for faster transportation has led to heavy congestion in road traffic networks, necessitating the need for traffic-responsive intelligent signal control systems. The developed signal control system based on uncertain information of the environment must be capable of determining the green time that minimizes the overall delay. The proposed traffic controller is based on distributed multi-agent approach which is very efficient considering the uncertainties and parameter variations. The effectiveness of the proposed control method is verified by comparing the obtained result with that of a fixed time controller.

SIGNAL (programming language)

Road traffic control

10.1109/iri.2012.6303051

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Citations (26)

New enhanced methods for radial basis function neural networks in function approximation

Mehdi Fatemi Mehdi Roopaei F. Shabaninia

Function approximation is a widely used method in system identification and recently RBF networks have been proposed as powerful tools for that. Existing algorithms suffer from some restrictions such as slow convergence and/or encountering to bias in parameter convergence. This paper is an attempt to improve the above problems by proposing new methods of parameter initializing and post-training to reach better capabilities in learning time and desired precision compared to previous RBF networks.

Initialization

Function Approximation

Identification

10.1109/ichis.2005.80

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Kalman-Particle Filter Used for Particle Swarm Optimization of Economic Dispatch Problem

Reza Khorshidi F. Shabaninia M. Vaziri S. Vadhva

This paper presents an effective evolutionary method to solve the Economic Dispatch (ED) problem with units having prohibited operating zones. The Kalman filter is an efficient recursive filter that estimates the state of a dynamic system from a series of noisy measurements in the Total Power Generation (TPG). ED is an example of a dynamic system algorithm that has been widely used for determination most economical generation profile to optimize the overall electricity prices. ED is a non-smooth problem when valve-point effects of generation units are considered. This paper applies Kalman - Particle filter (KF-PF) to the ED state estimation problem that has been optimized with Particle Swarm Optimization (PSO), with the emphasis to avoid the solution being trapped in local optimas [1], [2]. Kalman and particle filter are used to estimate TPG as state of ED problem. The performance of the KF-PF has been tested on a typical system and compared with others proposed in the literatures. The comparison results show that the efficiency of proposed approach can reach higher quality solutions.

Economic Dispatch

10.1109/ghtc.2012.73

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Citations (4)

Singular value decomposition assisted ensemble Kalman filter for history matching problem

Iranian Conference on Electrical Engineering (2011)

Mansoureh Jesmani F. Shabaninia Paknoosh Karimaghaee Afrooz Ebadat

The application of a recursive technique, the En-semble Kalman filter, to history matching problem has been considered. An algorithm is simplified in a way of reducing the order of the state vector by using Singular Value Decomposition (SVD). The state vector of the reservoir model is truncated to a few numbers of states corresponding to the largest singular values. The energy of the system is preserved by eliminating the state variables corresponding to the zero singular values or close to zero ones. In this way, the computational time is greatly reduced. Computational effort required for history matching of large reservoirs is a major problem. The algorithm of the proposed method is described by a synthetic model. Finally, it was evident from the simulation results that the application of the Ensemble Kalman filter combined with SVD significantly decreases the computational time of the estimation of unknown geological properties in large reservoirs. Furthermore, the example shows that the estimation results has improved in comparison with the results obtained with a much more expensive approach that estimates states in every grid blocks.

State vector

Singular value

Source

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Citations (1)

Petri net-based modeling and simulation of a hybrid manufacturing system

Proceedings of the IEEE Symposium on Emerging Technologies, 2005. (2005)

Abolfazl Jalilvand Sohrab Khanmohammadi F. Shabaninia

Dynamic systems with a mix of continuous and discrete components called hybrid systems frequently arise in engineering applications. By modeling these different components using differential equations and discrete events, it is possible to represent a wide range of phenomena present in physical and technological systems. Since many of these applications are safety critical, if is important to use reliable methods to simulate hybrid systems. In this paper we deal with modeling and simulation of hybrid systems using an extended form of timed Petri net. The proposed method is used for modeling of a robotic manufacturing system. The simulation results are obtained by a modified Petri net toolbox.

Toolbox

Stochastic Petri net

Process architecture

Discrete-Event Simulation

10.1109/icet.2005.1558912

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Citations (1)

PMU-based recursive state estimation and its performance with neural network

F. Shabaninia H. Sadeghi M. Vaziri S. Vadhva

Phasor measurement units (PMUs) are considered promising for future monitoring, protection and control of power systems. In this paper, an approach is proposed to achieve higher degrees of precision in state estimation using PMUs by recursive weighted least square method. Additionally, when neural network is used as a state estimator in conjunction with the PMUs, a method that uses less number of inputs is proposed for even higher precision of estimating the states as compared to conventional methods.

Units of measurement

State estimator

Phasor measurement unit

10.1109/pesgm.2012.6345075

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Citations (4)

Fuzzy type-2 electrode position controls for an Electric Arc Furnace

Mohammad Taslimian F. Shabaninia M. Vaziri S. Vadhva

This paper presents an approach to fuzzy type-2 Proportional-Integral-Derivative (PID) controls for the electrode positions in an Electric Arc Furnace (EAF). Due to the fluctuating power requirements and associated uncertainties of the EAF during operations, use of an accurate membership function is unreasonable. Therefore, fuzzy type-2 sets is applied for EAF controls. In this paper it is also proved that by fuzzy type-2 logic, the performance characteristics of the system will be improved using the proposed method.

Electric arc furnace

Position (finance)

Electric arc

10.1109/iri.2012.6303049

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Citations (9)

Designing ADC by Means of Soft Computing

Intelligent Automation & Soft Computing (2005)

F. Shabaninia Ebrahim Farjah Mohammad Samie

In this paper, the designing of ADC is studied by means of son computing. It is shown when fuzzy system has single input it can be multiplied in the premise rules by introducing proper fuzzy words and linguistic operator for amplifying knowledge base. Instinctive and learning behaviors, knowledge base and topology of circuit are found using fuzzy voronoi diagram in a POE model based program. This converter implements a set of processes during the analog to digital converting. It can be a fast ADC with more proper structure for hybrid neural network because its parameters are applied in training algorithms.

Soft Computing

10.1080/10798587.2005.10642904

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Citations (1)

Self-tuning PID control of liquid level system based on Fuzzy Wavelet Neural Network model

M. Davanipour Reza Dadkhah Tehrani F. Shabaninia

In this paper two objectives are followed; one is to illustrate the capability of Fuzzy Wavelet Neural Network (FWNN) in modeling; and the other is to propose a self-tuning PID controller based on this model. The controller is constructed by a FWNN structure combined with a PID controller. Gradient descent algorithm is used to accomplish tuning rules. The proposed method is applied for a nonlinear system, which is a liquid level system. The capability of FWNN in the modeling of nonlinear system by a few rules and susceptibility of the proposed controller will be shown by simulation. The convergence analysis of the algorithm will be done using Lyapunov theorem. It will be shown that the proposed method would increase the speed of tracking while having very little steady state error which is due to the high accuracy of fuzzy wavelet neural network.

10.1109/iraniancee.2016.7585575

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Citations (3)