Tolga Ensari

Global stability of a class of neural networks with time-varying delay

This paper presents a new sufficient condition for the uniqueness and global asymptotic stability of the equilibrium point for a class of neural networks with time-varying delays. The result is obtained by the use of a more general type of Lyapunov-Krasovskii functional, establishing a relation between the network parameters of the neural system and time-varying delay parameter. The result is also shown to be a generalization of a previously published result.

Global stability analysis of neural networks with multiple time varying delays

In this note, we study the equilibrium and stability properties of neural networks with time varying delays. Our main results give sufficient conditions for the existence, uniqueness and global asymptotic stability of the equilibrium point. The proposed conditions establish the relationships between network parameters of the neural systems and the delay parameters. The obtained results are applicable to all continuous nonmonotonic neuron activation functions and do not require the interconnection matrices to be symmetric. Some examples are also presented to compare our results with the previous results derived in the literature.

New results for robust stability of dynamical neural networks with discrete time delays

This paper deals with the global robust asymptotic stability of the equilibrium point of class of delayed neural networks having uncertain parameters whose values are unknown but bounded. By introducing a new upper bound norm for the interconnection matrix of the neural system and employing suitable Lyapunov functionals, we obtain new delay independent sufficient conditions for the uniqueness and global robust asymptotic stability of the equilibrium point. The obtained results can be easily verified as they can be expressed in terms of the network parameters only. Some examples are constructed to compare the reported results with the related existing literature results.

Correntropy-Based document clustering via nonnegative matrix factorization

Nonnegative Matrix Factorization (NMF) is one of the popular techniques to reduce the number of attributes of the data. It has been also widely used for clustering. Several types of the objective functions have been used for NMF in the literature. In this paper, we propose to maximize the correntropy similarity measure to produce the factorization itself. Correntropy is an entropy-based criterion defined as a nonlinear similarity measure. Following the discussion of minimization of the correntropy function, we use it to cluster document data set and compare its clustering performance with the Euclidean Distance (EucD)-based NMF. The comparison is illustrated with 20-Newsgroups data set. The results show that our approach has better clustering compared with other methods which use EucD as an objective function.

Occluded Face Recognition Using Correntropy-Based Nonnegative Matrix Factorization

Occluded face recognition is one the most interesting problems of applied computer vision. Among many face recognition approaches, the Nonnegative Matrix Factorization (NMF) turns out to be one of the popular techniques especially for part-based learning. It aims to factorize a nonnegative data matrix into two nonnegative matrices and obtains a well approximated product using an objective function. In this paper we propose to maximize the correntropy similarity measure as an objective function for NMF. Correntropy has been recently defined as a nonlinear similarity measure using an entropy-based criterion. After the minimization process of the correntropy function, we use it to recognize occluded face data set and compare its recognition performance with the standard NMF and Principal Component Analysis (PCA). The experimental results are illustrated with ORL face data set. The results show that our correntropy-based NMF (NMF-Corr) has better recognition rate compared with PCA and NMF.

Predictive cruise control

Predictive Cruise Control (PCC) is one of the most popular functionality on today vehicles. Briefly, it controls vehicle speed at the desired speed value determined by driver. In almost every vehicle sold today, cruise control could be found because it makes drivability manner easier and besides that it decreases fuel consumption with holding vehicle speed stable. Because of high popularity of cruise control, vehicle companies try to improve cruise control usage and also it is a good way to reduce fuel consumption. Therefore, new functionalities of cruise control become to emerge. One of these is predictive feature of cruise control or shortly PCC. PCC is an optimization problem for reducing fuel consumption and travel time and basically it is about finding the vehicle speed profile on a given slope and traffic profile of the road. Therefore, in this project, a PCC optimization problem is tried to solve with given road slope and traffic profile. Fuel consumption and time based cost functions are used and moreover dynamic programming structure is used for finding solution of optimization algorithm. As solution of the algorithm, vehicle speed profile is visualized with developing graphical user interface at the end of the study.

Performance analysis and improvement of machine learning algorithms for automatic modulation recognition over Rayleigh fading channels

Automatic modulation recognition (AMR) is becoming more important because it is usable in advanced general-purpose communication such as, cognitive radio, as well as, specific applications. Therefore, developments should be made for widely used modulation types; machine learning techniques should be employed for this problem. In this study, we have evaluated performances of different machine learning algorithms for AMR. Specifically, we have evaluated performances of artificial neural networks, support vector machines, random forest tree, k-nearest neighbor, Hoeffding tree, logistic regression, Naive Bayes and Gradient Boosted Regression Tree methods to obtain comparative results. The most preferred feature extraction methods in the literature have been used for a set of modulation types for general-purpose communication. We have considered AWGN and Rayleigh channel models evaluating their recognition performance as well as having made recognition performance improvement over Rayleigh for low SNR values using the reception diversity technique. We have compared their recognition performance in the accuracy metric, and plotted them as well. Furthermore, we have served confusion matrices for some particular experiments.

Graph-based concept discovery in multi relational data

Developments in technology, especially in computer science created the need of storing data in variety of areas. This need created the term database where the data is stored in a useful form. In the database, data is logically integrated in file/files according to relations among them. One of the important issues is to extract knowledge from these databases that hold data in a useful and complete form. This process is called as data mining. The main objective of data mining is to extract implicit and useful knowledge from huge and at first glance meaningless mass of data that is stored in database(s). Multi-Relational databases are the ones in which the data is stored in multiple tables (relations). The relationships between those tables are also stored as tables (relations) in the database. The more effective and commonly known approaches for Multi-Relational Data Mining (MRDM) are based on Inductive Logic Programming (ILP). ILP contains concepts from Inductive Learning and Logic Programming. From this point, the main purpose of MRDM is extracting implicit and trivial knowledge from relational database(s) using ILP approaches and techniques. In this approach, data is represented in graph structures and graph mining techniques are used for knowledge discovery. Concept discovery in multi-relational data mining aims to find relational rules that best describe a relation, called target relation, in terms of other relations in the database, called background knowledge. In this study, a graph-based concept discovery method for concept discovery is presented. The proposed method, namely G-CDS (Graph-based Concept Discovery System), utilizes methods both from substructure-based and path-finding based approaches, hence it can be considered as a hybrid method. G-CDS generates disconnected graph structures for each target relation and its related background knowledge, which are initially stored in a relational database, and utilizes them to guide generation of a summary graph. The summary graph is traversed to find concept descriptors. A set of experiments is conducted on datasets that belong to different learning problems. The experimental results show that G-CDS is capable of learning definitions of target relations that belong to different learning problems.

Evaluation of Machine Learning Algorithms for Automatic Modulation Recognition

Automatic modulation recognition (AMR) becomes more important because of usable in advanced general-purpose communication such as cognitive radio as well as specific applications. Therefore, developments should be made for widely used modulation types; machine learning techniques should be tried for this problem. In this study, we evaluate performance of different machine learning algorithms for AMR. Specifically, we propose nonnegative matrix factorization (NMF) technique and additionally we evaluate performance of artificial neural networks (ANN), support vector machines (SVM), random forest tree, k-nearest neighbor (k-NN), Hoeffding tree, logistic regression and Naive Bayes methods to obtain comparative results. These are most preferred feature extraction methods in the literature and they are used for a set of modulation types for general-purpose communication. We compare their recognition performance in accuracy metric. Additionally, we prepare and donate the first data set to University of California-Machine Learning Repository related with AMR.

Global asymptotic stability of a class of neural networks with time varying delays

This work presents a new sufficient condition for the uniqueness and global asymptotic stability (GAS) of the equilibrium point for a larger class of neural networks with time varying delays. It is shown that the use of a more general type of Lyapunov-Krasovskii functional leads to establish global asymptotic stability of a larger class of delayed neural networks that the neural network model considered in some previous papers.