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In this work we show that a metaheuristic, the variable neighborhood search (VNS), can be effectively used in order to improve the performance of the hardware-friendly version of the support vector machine (SVM). Our target is the implementation of the feed-forward phase of SVM on resource-limited hardware devices, such as field programmable gate arrays (FPGAs) and digital signal processors (DSPs)...
The purpose of this study was to develop a product design model for impact toughness estimation of low-alloy steel plates. Based on these estimates, the rejection probability of steel plates can be approximated. The target variable was formulated from three Charpy-V measurements with a LIB transformation, because the mean of the measurements would have lost valuable information.The method is suitable...
We present an analytic and geometric view of the sample mean of graphs. The theoretical framework yields efficient subgradient methods for approximating a structural mean and a simple plug-in mechanism to extend existing central clustering algorithms to graphs. Experiments in clustering protein structures show the benefits of the proposed theory.
This paper presents a recurrent neural observer to estimate substrate and biomass concentrations in an activated sludge wastewater treatment. The observer is based on a discrete-time high order neural network (RHONN) trained on-line with an extended Kalman filter (EKF)-based algorithm. This observer is then associated with a hybrid intelligent system to control the substrate/biomass concentration...
Sensory inputs such as visual images or audio spectrograms can act as symbols in a new cognitive model. The stability of direct image association operators allows the discrete bit patterns in a general-purpose symbol processing system to be replaced with continuous real-world signals. Analogous to an SR flip-flop, two reciprocal images recursively connected by association processors, can ldquolockrdquo...
A nonlinear discrete-time neural observer for the state estimation of a discrete-time induction motor model, in presence of external and internal uncertainties is presented. The observer is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm. This observer estimates the state of the unknown discrete-time nonlinear system,...
Since machine learning has become a tool to make more efficient design of sophisticated systems, we present in this paper a novel methodology to create powerful neural network controllers for complex systems while minimising the design effort. Using a robot task as a case study, we have shown that using the feedback from the robot itself, the system can learn from experience, or example provided by...
The operation of V1 simple cells in primates has been traditionally modelled with linear models resembling Gabor filters, whereas the functionality of subsequent visual cortical areas is less well understood. Here we explore the learning of mechanisms for further nonlinear processing by assuming a functional form of a product of two linear filter responses, and estimating a basis for the given visual...
Document representation is one of the crucial components that determine the effectiveness of text classification tasks. Traditional document representation approaches typically adopt a popular bag-of-word method as the underlying document representation. Although itpsilas a simple and efficient method, the major shortcoming of bag-of-word representation is in the independent of word feature assumption...
The study of the neuronal correlates of the spontaneous alternation in perception elicited by bistable visual stimuli is promising for understanding the mechanism of neural information processing and the neural basis of visual perception and perceptual decision-making. In this paper we apply a sparse nonnegative tensor factorization (NTF) based method to extract features from the local field potential...
Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics, medical practices, geographic scene analysis, and many others. One of the primary challenges is detection and recognition of objects in the presence of transformations such as resolution, rotation, translation, scale and occlusion...
We present a pattern recognition methodology based on stochastic logic. The technique implements a parallel comparison of input data from a set of sensors to various pre-stored categories. Smart pulse-based stochastic-logic blocks are constructed to provide an efficient architecture that is able to implement Bayesian techniques, thus providing a low-cost solution in terms of gate count and power dissipation...
In this paper, we investigate the wellposedness of the kernel adaline. The kernel adaline finds the linear coefficients in a radial basis function network using deterministic gradient descent. We will show that the gradient descent provides an inherent regularization as long as the training is properly early-stopped. Along with other popular regularization techniques, this result is investigated in...
We address the problem of selecting and extracting key features by using singular value decomposition and latent semantic analysis. As a consequence, we are able to discover latent information which allows us to design signatures for forensics and in a dual approach for real-time intrusion detection systems. The validity of this method is shown by using several automated classification algorithms...
An improved classified image interpolation algorithm is presented. The algorithm obtains high-resolution pixels by filtering with parameters that are optimal for the selected class. By applying the highly flexible neural network model in the proposed algorithms, classified image data is extended into a nonlinear model in each class while enhancing the sharpness and edge characteristic. Meantime the...
An extremely simple technique for training the weights of a feedforward multilayer neural network is described and tested The method, dubbed ldquoneighbor annealingrdquo is a simple random walk through weight space with a gradually decreasing step size. The approach is compared against backpropagation and particle swarm optimization on a variety of training tasks. Neighbor annealing is shown to perform...
A modular neural network works by dividing the input domain into segments, assigning a separate neural network to each sub-domain. This paper introduces the self-splitting modular neural network, in which the partitioning of the input domain occurs during training. It works by first attempting to solve a problem with a single network. If that fails, it finds the largest chunk of the input domain that...
A class of resource location service for distributed VoD system, which combines one-hop k-random walk and global centralized indexing service, is studied. First, in order to minimizing the cost of communication and guaranteeing the response time performance, a Markov model is proposed to describe the queue phenomenon, admission control and the process of location. In this model, control is related...
Clustering with constraints is an active area in machine learning and data mining. In this paper, a semi-supervised kernel-based fuzzy C-means algorithm called PCKFCM is proposed which incorporates both semi-supervised learning technique and the kernel method into traditional fuzzy clustering algorithm. The clustering is achieved by minimizing a carefully designed objective function. A kernel-based...
In recent years, 3D virtual reality technology has been growing fast. The user interface of the Internet is facing a new challenge. Many organizations have begun to propose various kinds of specifications and standards for Web3D. The specification draft of VRML1.0 was proposed in 1994. In 2000, Java3D, Extensible 3D (X3D) and MPEG-4 Binary Format for Scene (BIFS) were formally included as important...
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