DD-HDS: A Method for Visualization and Exploration of High-Dimensional Data
Mapping high-dimensional data in a low-dimensional space, for example, for visualization, is a problem of increasingly major concern in data analysis. This paper presents data-driven high-dimensional scaling (DD-HDS), a nonlinear mapping method that ...
Sensitivity Analysis of the Split-Complex Valued Multilayer Perceptron Due to the Errors of the i.i.d. Inputs and Weights
In this paper, we analyze the sensitivity of a split-complex multilayer perceptron (split-CMLP) due to the errors of the inputs and the connection weights between neurons. For simplicity, all the inputs and weights studied here are independent and ...
Localized Generalization Error Model and Its Application to Architecture Selection for Radial Basis Function Neural Network
The generalization error bounds found by current error models using the number of effective parameters of a classifier and the number of training samples are usually very loose. These bounds are intended for the entire input space. However, support ...
Polytope ARTMAP: Pattern Classification Without Vigilance Based on General Geometry Categories
This paper proposes polytope ARTMAP (PTAM), an adaptive resonance theory (ART) network for classification tasks which does not use the vigilance parameter. This feature is due to the geometry of categories in PTAM, which are irregular polytopes whose ...
Markov and Semi-Markov Switching of Source Appearances for Nonstationary Independent Component Analysis
Independent component analysis (ICA) is currently the most popularly used approach to blind source separation (BSS), the problem of recovering unknown source signals when their mixtures are observed but the actual mixing process is unknown. Many ICA ...
Discrete-Time Analogs for a Class of Continuous-Time Recurrent Neural Networks
This paper is concerned with the problem of local and global asymptotic stability for a class of discrete-time recurrent neural networks, which provide discrete-time analogs to their continuous-time counterparts, i.e., continuous-time recurrent neural ...
Separating Points by Parallel Hyperplanes— Characterization Problem
This paper deals with partitions of a discrete set S of points in a d-dimensional space, by h parallel hyperplanes. Such partitions are in a direct correspondence with multilinear threshold functions which appear in the theory of neural networks and ...
Collective Behavior of a Small-World Recurrent Neural System With Scale-Free Distribution
This paper proposes a scale-free highly clustered echo state network (SHESN). We designed the SHESN to include a naturally evolving state reservoir according to incremental growth rules that account for the following features: (1) short characteristic ...
Delayed Standard Neural Network Models for Control Systems
In order to conveniently analyze the stability of recurrent neural networks (RNNs) and successfully synthesize the controllers for nonlinear systems, similar to the nominal model in linear robust control theory, the novel neural network model, named ...
Robust Adaptive Observer Design for Uncertain Systems With Bounded Disturbances
This paper presents a robust adaptive observer design methodology for a class of uncertain nonlinear systems in the presence of time-varying unknown parameters with absolutely integrable derivatives, and nonvanishing disturbances. Using the universal ...
Local Model Network Identification With Gaussian Processes
A Bayesian Gaussian process (GP) modeling approach has recently been introduced to model-based control strategies. The estimate of the variance of the predicted output is the most useful advantage of GPs in comparison to neural networks (NNs) and fuzzy ...
A Modified Probabilistic Neural Network for Partial Volume Segmentation in Brain MR Image
A modified probabilistic neural network (PNN) for brain tissue segmentation with magnetic resonance imaging (MRI) is proposed. In this approach, covariance matrices are used to replace the singular smoothing factor in the PNN's kernel function, and ...
Maximization of Mutual Information for Supervised Linear Feature Extraction
In this paper, we present a novel scheme for linear feature extraction in classification. The method is based on the maximization of the mutual information (MI) between the features extracted and the classes. The sum of the MI corresponding to each of ...
Bilinear Analysis for Kernel Selection and Nonlinear Feature Extraction
This paper presents a unified criterion, Fisher kernel criterion (FKC), for feature extraction and recognition. This new criterion is intended to extract the most discriminant features in different nonlinear spaces, and then, fuse these features under a ...
Weighted Mahalanobis Distance Kernels for Support Vector Machines
The support vector machine (SVM) has been demonstrated to be a very effective classifier in many applications, but its performance is still limited as the data distribution information is underutilized in determining the decision hyperplane. Most of the ...
String Tightening as a Self-Organizing Phenomenon
The phenomenon of self-organization has been of special interest to the neural network community throughout the last couple of decades. In this paper, we study a variant of the self-organizing map (SOM) that models the phenomenon of self-organization of ...
Implementing Spiking Neural Networks for Real-Time Signal-Processing and Control Applications: A Model-Validated FPGA Approach
In this paper, we present two versions of a hardware processing architecture for modeling large networks of leaky-integrate-and-flre (LIF) neurons; the second version provides performance enhancing features relative to the first. Both versions of the ...
Neural Network Implementation Using Bit Streams
A new method for the parallel hardware implementation of artificial neural networks (ANNs) using digital techniques is presented. Signals are represented using uniformly weighted single-bit streams. Techniques for generating bit streams from analog or ...
Analysis and Online Realization of the CCA Approach for Blind Source Separation
A critical analysis of the canonical correlation analysis (CCA) approach in blind source separation (BSS) is provided. It is proved that by maximizing the autocorrelation functions of the recovered signals we can separate the source signals ...
Stochastic Gradient-Adaptive Complex-Valued Nonlinear Neural Adaptive Filters With a Gradient-Adaptive Step Size
A class of variable step-size learning algorithms for complex-valued nonlinear adaptive finite impulse response (FIR) filters is proposed. To achieve this, first a general complex-valued nonlinear gradient-descent (CNGD) algorithm with a fully complex ...
Blind Source Extraction Using Generalized Autocorrelations
This letter addresses blind (semiblind) source extraction (BSE) problem when a desired source signal has temporal structures, such as linear or nonlinear autocorrelations. Using the temporal characteristics of sources, we develop objective functions ...
A Quick Assessment of Topology Preservation for SOM Structures
Several topology preservation measures and monitoring schemes have been proposed to help ascertain the correct organization of the self-organizing map (SOM) structure. Here, we consider a novel idea that performs faster than previous alternatives while ...
Connections Between Score Matching, Contrastive Divergence, and Pseudolikelihood for Continuous-Valued Variables
Score matching (SM) and contrastive divergence (CD) are two recently proposed methods for estimation of nonnormalized statistical methods without computation of the normalization constant (partition function). Although they are based on very different ...
A New Constrained Independent Component Analysis Method
Constrained independent component analysis (cICA) is a general framework to incorporate a priori information from problem into the negentropy contrast function as constrained terms to form an augmented Lagrangian function. In this letter, a new improved ...
Identification of Nonlinear Systems With Unknown Time Delay Based on Time-Delay Neural Networks
In this letter, we address the problem of online identification of nonlinear continuous-time systems with unknown time delay based on neural networks (NNs). A novel time-delay NN model with learning algorithm is employed to perform simultaneous system ...
Working Set Selection Using Functional Gain for LS-SVM
The efficiency of sequential minimal optimization (SMO) depends strongly on the working set selection. This letter shows how the improvement of SMO in each iteration, named the functional gain (FG), is used to select the working set for least squares ...
A Geometric Nearest Point Algorithm for the Efficient Solution of the SVM Classification Task
Geometric methods are very intuitive and provide a theoretically solid approach to many optimization problems. One such optimization task is the support vector machine (SVM) classification, which has been the focus of intense theoretical as well as ...
