Behzad Moshiri

H∞ control of parameter-dependent state-delayed systems using polynomial parameter-dependent quadratic functions

This paper presents the issue of robust disturbance attenuation and robust asymptotic stability problem for finite-dimensional linear parameter-dependent state-delayed systems. The use of polynomial parameter-dependent quadratic Lyapunov functions and linear matrix inequalities (LMIs) formulations for robust H ∞ control are considered. It is shown that the state feedback control can be determined to guarantee the stability of the closed-loop system independently of the time-delay. We present an illustrative example to demonstrate the applicability of the proposed design approach.

A computational method for solving optimal control and parameter estimation of linear systems using Haar wavelets

In this article, a computational method based on Haar wavelet in time-domain for solving the problem of optimal control of the linear time invariant systems for any finite time interval is proposed. Haar wavelet integral operational matrix and the properties of Kronecker product are utilized to find the approximated optimal trajectory and optimal control law of the linear systems with respect to a quadratic cost function by solving only the linear algebraic equations. It is shown that parameter estimation of linear system can be done easily using the idea proposed. On the basis of Haar function properties, the results of the article, which include the time information, are illustrated in two examples.

Numerically efficient approximations to the optimal control of linear singularly perturbed systems based on Haar wavelets

In this paper we present an implementation of the Haar wavelet to the optimal control of linear singularly perturbed systems. The approximated composite control and the slow and fast trajectories with respect to a quadratic cost function are calculated by solving only the linear algebraic equations. The results are illustrated with a simple example.

Prediction of protein submitochondria locations based on data fusion of various features of sequences

In this study, the predictors are developed for protein submitochondria locations based on various features of sequences. Information about the submitochondria location for a mitochondria protein can provide much better understanding about its function. We use ten representative models of protein samples such as pseudo amino acid composition, dipeptide composition, functional domain composition, the combining discrete model based on prediction of solvent accessibility and secondary structure elements, the discrete model of pairwise sequence similarity, etc. We construct a predictor based on support vector machines (SVMs) for each representative model. The overall prediction accuracy by the leave-one-out cross validation test obtained by the predictor which is based on the discrete model of pairwise sequence similarity is 1% better than the best computational system that exists for this problem. Moreover, we develop a method based on ordered weighted averaging (OWA) which is one of the fusion data operators. Therefore, OWA is applied on the 11 best SVM-based classifiers that are constructed based on various features of sequence. This method is called Mito-Loc. The overall leave-one-out cross validation accuracy obtained by Mito-Loc is about 95%. This indicates that our proposed approach (Mito-Loc) is superior to the result of the best existing approach which has already been reported.

Forecasting of short-term traffic-flow based on improved neurofuzzy models via emotional temporal difference learning algorithm

Bounded rationally idea, rather that optimization idea, have result and better performance in decision making theory. Bounded rationality is the idea in decision making, rationality of individuals is limited by the information they have, the cognitive limitations of their minds, and the finite amount of time they have to make decisions. The emotional theory is an important topic presented in this field. The new methods in the direction of purposeful forecasting issues, which are based on cognitive limitations, are presented in this study. The presented algorithms in this study are emphasizes to rectify the learning the peak points, to increase the forecasting accuracy, to decrease the computational time and comply the multi-object forecasting in the algorithms. The structure of the proposed algorithms is based on approximation of its current estimate according to previously learned estimates. The short term traffic flow forecasting is a real benchmark that has been studied in this area. Traffic flow is a good measure of traffic activity. The time-series data used for fitting the proposed models are obtained from a two lane street I-494 in Minnesota City, USA. The research discuss the strong points of new method based on neurofuzzy and limbic system structure such as Locally Linear Neurofuzzy network (LLNF) and Brain Emotional Learning Based Intelligent Controller (BELBIC) models against classical and other intelligent methods such as Radial Basis Function (RBF), Takagi-Sugeno (T-S) neurofuzzy, and Multi-Layer Perceptron (MLP), and the effect of noise on the performance of the models is also considered. Finally, findings confirmed the significance of structural brain modeling beyond the classical artificial neural networks.

Fusing a Bluetooth Traffic Monitoring System With Loop Detector Data for Improved Freeway Traffic Speed Estimation

Anonymous probe vehicle monitoring systems are being developed to measure travel times on highways and arterials based on wireless signals available from technologies such as Bluetooth. Probe vehicle data can provide accurate measurements of current traffic speeds and travel times due to their excellent spatial coverage. However, presently probe vehicles are only a small portion of the vehicles that make up all of the traffic in the network. Alternatively, data from conventional loop detectors cover almost all the vehicles that have traveled along a road section, resulting in excellent temporal coverage. Unfortunately, loop detector measurements can be imprecise; their spatial sampling depends on the loop detector spacing, and they typically only represent traffic speed at the location of the detector and not over the entire road segment. With this complementarity in mind, this article explores several data fusion techniques for fusing data from these sources together. All methods are implemented and compared in terms of their ability to fuse data from loop detectors and probe vehicles to accurately estimate freeway traffic speeds. Data from a Bluetooth traffic monitoring system are fused with corresponding loop detector data and compared against GPS collected probe vehicle data on a stretch of Highway 401 in Toronto, Canada. The analysis shows that through data fusion, even a few probe vehicle measurements from a Bluetooth traffic monitoring system can improve the accuracy of traffic speed estimates traditionally obtained from loop detectors.

Pseudo information measure: a new concept for extension of Bayesian fusion in robotic map building

Abstract A new concept named pseudo information measure is introduced. By this measure, Bayesian fusion of independent sources of information is extended to a wide range of possible formulations and some new fusion formulas are calculated. The coincidence between the performance of the proposed method of fusion with the expected results and output sensitivity of the fusion process are discussed. Also, we have discussed the resulting flexibility for map building applications. Map building by using the proposed fusion formulas has been simulated for a cylindrical robot with eight ultrasonic sensors and implemented on Khepera robot. The resulting maps have been fed to an improved version of A* path planning for comparative purposes. For the resulting routes, two factors have been considered and calculated: length and the least distance to obstacles. The results show that the maps of the environment that are generated by using the proposed fusion formulas are more informative. In addition, more appropriate routes are achieved. Based on the selected function, there is a trade-off between the length of the resulting routes and their safety. This flexibility lets us choose the right fusion function for different map building applications.

Improve text classification accuracy based on classifier fusion methods

Naive-Bayes and k-NN classifiers are two machine learning approaches for text classification. Rocchio is the classic method for text classification in information retrieval. Based on these three approaches and using classifier fusion methods, we propose a novel approach in text classification. Our approach is a supervised method, meaning that the list of categories should be defined and a set of training data should be provided for training the system. In this approach, documents are represented as vectors where each component is associated with a particular word. We proposed voting methods and OWA operator and decision template method for combining classifiers. Experimental results show that these methods decrese the classification error 15 percent as measured on 2000 training data from 20 newsgroups dataset.

Research Article: A protein fold classifier formed by fusing different modes of pseudo amino acid composition via PSSM

Protein function is related to its chemical reaction to the surrounding environment including other proteins. On the other hand, this depends on the spatial shape and tertiary structure of protein and folding of its constituent components in space. The correct identification of protein domain fold solely using extracted information from protein sequence is a complicated and controversial task in the current computational biology. In this article a combined classifier based on the information content of extracted features from the primary structure of protein has been introduced to face this challenging problem. In the first stage of our proposed two-tier architecture, there are several classifiers each of which is trained with a different sequence based feature vector. Apart from the application of the predicted secondary structure, hydrophobicity, van der Waals volume, polarity, polarizability, and different dimensions of pseudo-amino acid composition vectors in similar studies, the position specific scoring matrix (PSSM) has also been used to improve the correct classification rate (CCR) in this study. Using K-fold cross validation on training dataset related to 27 famous folds of SCOP, the 28 dimensional probability output vector from each evidence theoretic K-NN classifier is used to determine the information content or expertness of corresponding feature for discrimination in each fold class. In the second stage, the outputs of classifiers for test dataset are fused using Sugeno fuzzy integral operator to make better decision for target fold class. The expertness factor of each classifier in each fold class has been used to calculate the fuzzy integral operator weights. Results make it possible to provide deeper interpretation about the effectiveness of each feature for discrimination in target classes for query proteins.

Introducing adaptive neurofuzzy modeling with online learning method for prediction of time-varying solar and geomagnetic activity indices

Research in space weather has in recent years become an active field of research requiring international cooperation because of its importance in hazard warning especially for satellite technology and power utility systems. The time-varying sun as the main source of space weather impacts the Earths magnetosphere by emitting hot magnetized plasma called solar wind into interplanetary space. The emission of Solar Energetic Particles (SEPs) and consequently the magnitude of Interplanetary Magnetic Field (IMF) vary almost periodically with an approximate life cycle of 11years. It is shown that the solar and geomagnetic activity indices have complex behavior often characterizable as quasi-periodic or even chaotic, which causes the long-term prediction to be a conundrum. Moreover, solar and geomagnetic activity indices and their chaotic characteristics vary abruptly during solar and geomagnetic storms. This variation depicts the difficulties in modeling and long-term prediction of solar and geomagnetic storms. On the other hand, neural networks and related neurofuzzy tools as general function approximators have been the subjects of interest due to their many practical applications in modeling and predicting complex phenomena. However, most of these systems are trained by algorithms that need to be carried out by an off-line data set which influence their performance in prediction of time-varying solar and geomagnetic activity indices. This paper proposes an adaptive neurofuzzy approach with a recursive learning algorithm for modeling and prediction of space weather indices which fulfill requirements of prediction of time-varying solar and geomagnetic activities for long time spans. The obtained results depict the power of the proposed method in online prediction of time-varying solar and geomagnetic activity indices.