IEEE Transactions on Neural Networks and Learning Systems

Cognitive fault diagnosis systems differentiate from more traditional solutions by providing online strategies to create and update the fault-free and the faulty classes directly from incoming data. This aspect is of paramount relevance within the big data framework, since measurements are there immediately processed to detect and identify the upsurge of potential faults. This paper introduces a n... View full abstract»

Several correlated issues of autonomy and simplicity regarding formation control for robots with a self-awareness mechanism in unstructured environments are considered. To achieve autonomy and simplicity, a hybrid scheme is derived for robot maneuvering based on a multibehavioral system. The system holds some self-awareness capabilities ensuring precision and robustness in the presence of internal... View full abstract»

In this paper, we study the global asymptotic robust stability of delayed neural networks with norm-bounded uncertainties. By employing the Lyapunov stability theory and homeomorphic mapping theorem, we derive some new types of sufficient conditions ensuring the existence, uniqueness, and global asymptotic stability of the equilibrium point for the class of neural networks with discrete time delay... View full abstract»

A major drawback of nonlinear dimensionality reduction (DR) techniques is their inability to preserve some authentic information from the source domain, leading to projections that are often hard to interpret when it comes to observing anything other than the topological structure of the data. In this paper, we propose a nonlinear DR approach enforcing projection constraints resulting from an a pr... View full abstract»

Learning sparse representation of high-dimensional data is a state-of-the-art method for modeling data. Matrix factorization-based techniques, such as nonnegative matrix factorization and concept factorization (CF), have shown great advantages in this area, especially useful for image representation. Both of them are linear learning problems and lead to a sparse representation of the images. Howev... View full abstract»

The recent literature indicates that preserving global pairwise sample similarity is of great importance for feature selection and that many existing selection criteria essentially work in this way. In this paper, we argue that besides global pairwise sample similarity, the local geometric structure of data is also critical and that these two factors play different roles in different learning scen... View full abstract»

Clustering is widely used in machine learning, feature extraction, pattern recognition, image analysis, information retrieval, and bioinformatics. Online unsupervised incremental learning is an important branch of data clustering. However, accurately separating high-density overlapped areas in a network has a direct impact on the performance of the clustering algorithm. In this paper, we propose a... View full abstract»

The problem of optimal switching and control of switching systems with nonlinear subsystems is investigated in this paper. An approximate dynamic programming-based algorithm is proposed for learning the optimal cost-to-go function based on the switching instants and the initial conditions. The global optimal switching times for every selected initial condition are directly found through the minimi... View full abstract»

Feature selection in learning to rank has recently emerged as a crucial issue. Whereas several preprocessing approaches have been proposed, only a few have focused on integrating feature selection into the learning process. In this paper, we propose a general framework for feature selection in learning to rank using support vector machines with a sparse regularization term. We investigate both cla... View full abstract»

Multiset features extracted from the same patterns always represent different characteristics of data. Thus, it is very valuable to perform the extraction on multiple feature sets. This paper addresses the issue of multiset correlation feature extraction (MCFE) in multiple feature representations. A novel method is proposed to carry out the MCFE for classification, called multiset canonical correl... View full abstract»

We present an approach to learn the inverse kinematics of the “bionic handling assistant”-an elephant trunk robot. This task comprises substantial challenges including high dimensionality, restrictive and unknown actuation ranges, and nonstationary system behavior. We use a recent exploration scheme, online goal babbling, which deals with these challenges by bootstrapping and adapting the inverse ... View full abstract»

This paper is concerned with global asymptotic stability of a class of Cohen-Grossberg bidirectional associative memory (BAM) neural networks with delays. Under the assumptions that the activation functions only satisfy the so-called extended global Lipschitz condition and the behaved functions only satisfy global Lipschitz condition, we apply linear matrix inequality (LMI) method and homeomorphis... View full abstract»

This paper introduces a novel and well-founded betweenness measure, called the bag-of-paths (BoP) betweenness, as well as its extension, the BoP group betweenness, to tackle semisupervised classification problems on weighted directed graphs. The objective of semisupervised classification is to assign a label to unlabeled nodes using the whole topology of the graph and the labeled nodes at our disp... View full abstract»

In this paper, a rapid detection and isolation scheme for oscillation faults in a distributed nonlinear system is proposed. The distributed nonlinear system considered is modeled as a set of interconnected subsystems. First, a local learning and merging method based on deterministic learning theory is proposed to obtain knowledge of the unknown interconnections and the fault functions. Second, usi... View full abstract»

This paper presents a Gaussian classifier-based evolutionary strategy (GCES) to solve multimodal optimization problems. An evolutionary technique for them must answer two crucial questions to guarantee its success: how to distinguish among the different basins of attraction and how to safeguard the already discovered good-quality solutions including both global and local optima. In GCES, multimoda... View full abstract»

Because of the complexity of consensus control of nonlinear multiagent systems in state time-delay, most of previous works focused only on linear systems with input time-delay. An adaptive neural network (NN) consensus control method for a class of nonlinear multiagent systems with state time-delay is proposed in this paper. The approximation property of radial basis function neural networks (RBFN... View full abstract»