Voting among virtually generated versions of a classification problem

A classifier combining strategy, virtual voting by random projection (VVRP), is presented. VVRP takes advantage from the bounded distortion incurred by random projection in order to improve accuracies of stable classifiers like discriminant analysis (DA) where existing classifier combining strategies are known to be failed. It uses the distortion to virtually generate different training sets from the total available training samples in a way that does not have the potential for overfitting. Then, a majority voting combines the base learners trained on these versions of the original problem. VVRP is very simple and just needs determining a proper dimensionality for the versions, an often very easy task. It is shown to be stable in a very large region of the hyperplane constructed by the dimensionality and the number of the versions. VVRP improves the best state-of-the-art DA algorithms in both small and large sample size problems in various classification fields.     

Incorporation of a Regularization Term to Control Negative Correlation in Mixture of Experts

Combining accurate neural networks (NN) in the ensemble with negative error correlation greatly improves the generalization ability. Mixture of experts (ME) is a popular combining method which employs special error function for the simultaneous training of NN experts to produce negatively correlated NN experts. Although ME can produce negatively correlated experts, it does not include a control parameter like negative correlation learning (NCL) method to adjust this parameter explicitly. In this study, an approach is proposed to introduce this advantage of NCL into the training algorithm of ME, i.e., mixture of negatively correlated experts (MNCE). In this proposed method, the capability of a control parameter for NCL is incorporated in the error function of ME, which enables its training algorithm to establish better balance in bias-variance-covariance trade-off and thus improves the generalization ability. The proposed hybrid ensemble method, MNCE, is compared with their constituent methods, ME and NCL, in solving several benchmark problems. The experimental results show that our proposed ensemble method significantly improves the performance over the original ensemble methods.     

Direct adaptive fuzzy control with membership function tuning

In this paper, a direct self‐structured adaptive fuzzy control is introduced for the class of nonlinear systems with unknown dynamic models. Control is accomplished by an adaptive fuzzy system with a fixed number of rules and adaptive membership functions. The reference signal and state errors are used to tune the membership functions and update them instantaneously. The Lyapunov synthesis method is also used to guarantee the stability of the closed loop system. The proposed control scheme is applied to an inverted pendulum and a magnetic levitation system, and its effectiveness is shown via simulation. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Adaptive synchronization of master–slave systems with mixed neutral and discrete time‐delays and nonlinear perturbations

This paper investigates the delay‐dependent adaptive synchronization problem of the master and slave structure of linear systems with both constant neutral and time‐varying discrete time‐delays and nonlinear perturbations based on the Barbalat lemma and matching conditions. An adaption law which includes the master‐slave parameters is obtained by using the Lyapunov functional method and inequality techniques to synchronize the master‐slave systems without the knowledge of upper bounds of perturbation terms. Particularly, it is shown that the synchronization speed can be controlled by adjusting the update gain of the synchronization signal. A numerical example has been given to show the effectiveness of the method. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

A robust critical path in an environment with hybrid uncertainty

In this article, we consider the project critical path problem in an environment with hybrid uncertainty. In this environment, the duration of activities are considered as random fuzzy variables that have probability and fuzzy natures, simultaneously. To obtain a robust critical path with this kind of uncertainty a chance constraints programming model is used. This model is converted to a deterministic model in two stages. In the first stage, the uncertain model is converted to a model with interval parameters by alpha-cut method and distribution function concepts. In the second stage, the interval model is converted to a deterministic model by robust optimization and min-max regret criterion and ultimately a genetic algorithm with a proposed exact algorithm are applied to solve the final model. Finally, some numerical examples are given to show the efficiency of the solution procedure.     

Prediction of scouring around an arch-shaped bed sill using Neuro-Fuzzy model

In this study, Adaptive Neuro-Fuzzy Inference System (ANFIS) has been used to model local scouring depth and pattern scouring around concave and convex arch shaped circular bed sills. The experimental part of this research study includes seven sets of laboratory test cases which were performed in an experimental flume under different flow conditions. A data set consists of 2754 data points of scouring depth were collected to use in the ANFIS model. The ratio of arch diameter, D, to flume width, W, is used as a non dimensional variables in all test cases. The results from ANFIS model were compared with the results of ANN model obtained by Homayoon et al. [24] and previously presented models. The results indicated that for D/W equal to 1 and 1.2, the ANFIS models produced a good performance for convex and concave bed sills. As a result, the ANFIS models can be used as an alternative to ANN for estimation of scour depth and scour pattern around a concave bed sill installed with a bridge pier.     

A predictive controller based on dynamic matrix control for a non-minimum phase robot manipulator

In this paper a hybrid control strategy is presented based on Dynamic Matrix Control (DMC) and feedback linearization methods for designing a predictive controller of five bar linkage manipulator as a MIMO system (two inputs and two outputs). Analyzing the internal dynamic of robot shows the open loop system is unstable and non-minimum phase, so in order to apply the predictive controller, special modifications are needed. These modifications on non-minimum phase behavior are performed using feedback linearization procedure based on state space realization. The design objective is to track a desirable set point as well as time varying trajectories as a command references with globally asymptotical stabilization. The proposed controller is applied to nonlinear fully coupled model of the typical five bar linkage manipulator with non-minimum phase behavior. Simulation results show that the proposed controller has good efficiency. The step responses of system with and without feedback linearization process illustrated that the mentioned modification for stabilizing is performed properly. After applying the proposed predictive controller, the joint angle of robot tracks the reference input while another input acts as the disturbance and vice versa.     

A hierarchical fuzzy system for modeling driver’s behavior

The study of human behavior during driving is of primary importance for improving the driver??s security. In this study, we propose a hierarchical driver_vehicle_environment fuzzy system to analyze driver??s behavior under stress conditions on a road. We include climate, road and car conditions in fuzzy modeling. For obtaining fuzzy rules, experts?? opinions are benefited by means of questionnaires on effects of parameters such as climate, road and car conditions on driving capabilities. The number of fuzzy rules is optimized by Particle Swarm Optimization (PSO) algorithm. Also the frequency of pressing on brake and gas pedals and the number of car??s direction changes are used to determine the driver??s behavior under different conditions. Three different positions are considered for driving and decision making; one position in driving lane and two positions in opposite lane. A fuzzy model called Model 1 is presented for modeling the change of steering angle and speed control by considering time distances with existing cars in these three positions, the information about the speed and direction of car, and the steering angle of car. The behaviors of different drivers under two stress conditions are investigated. Also we obtained two other models based on fuzzy rules called Model 2 and Model 3 by using Sugeno fuzzy inference. Model 2 has two linguistic terms and Model 3 has four linguistic terms for estimating the time distances with other cars. The results of three models are compared. The comparative studies have shown that simulation results are in good agreement with the real world situations.     

Parameter identification and design of a robust attitude controller using <Emphasis Type="Italic">H</Emphasis><Subscript>∞</Subscript> methodology for the raptor E620 small-scale helicopter

Rigorous control synthesis for an unmanned aerial vehicle necessitates the availability of a good, reasonable model for such a vehicle. The work reported in this paper focuses on the modeling of a rotary unmanned aerial vehicle (RUAV) and the development of a robust controller that can handle parameter uncertainties and disturbances. The parameters of the plant model are obtained through the use of the prediction error method with real flight data. The response of the identified linear model shows a good match with the measured flight data. The H _∞ control scheme is applied to obtain a robustly stable controller using the identified model. The proposed controller is analyzed using frequency-domain analysis and time-domain simulations. The performance of the proposed H _∞ controller is better than that of the conventional proportional derivative controller in that the proposed controller has a shorter settling time and less overshoot. Furthermore, the degradation of the proposed controller performance is negligible and stability is maintained when the input gains to the plant are doubled, which demonstrates the good performance and robustness of the controller.     

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