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Computational modeling of neural circuits enhances our comprehension of brain functions. In addition to the simulation of the models which helps to anticipate cognitive processes, embodiment of these models is essential. Such embodiment would provide the setting to explain neural functioning ongoing in real environments under oncoming sensory information besides giving opportunity of implementation...
This paper proposes a Simulated Annealing enhanced Greed Vacancy Search Algorithm (eGVXSA) for solving packing equal circles in a damaged square. The damaged area is consisted of a number of small square regions that are randomly distributed within the container. The benefits present at its efficiency of consuming limited space. For this problem, Experiments have shown the efficiency over the original...
Non-negative Matrix Factorization (NMF) is a method of multivariate analysis which factorizes a non-negative matrix into two non-negative matrices. While conventional NMF algorithms use the Euclidian distance or the Kullback-Leibler divergence as cost functions, those methods fail to extract latent structure or interpretable information from the matrix when the target matrix is contaminated by noise...
In recent years, water quality prediction has attracted many attentions of governments and researchers. The safety of water quality seriously affects the human health, fishery economy and agricultural activities. If an early prediction to the water quality with an acceptable accuracy can be achieved, the negative impacts will be minimized or even be avoided. Many researchers have applied artificial...
In this paper, we develop a novel constrained recursive least squares algorithm for adaptively combining a set of given multiple models. With data available in an online fashion, the linear combination coefficients of submodels are adapted via the proposed algorithm.We propose to minimize the mean square error with a forgetting factor, and apply the sum to one constraint to the combination parameters...
In this paper, we address the issue of dealing with huge amounts of data from recordings of an Electroencephalogram (EEG) in epileptic patients. In particular, the attention is focused on the development of tools to support the neurophysiologists in the time consuming and challenging task of reviewing the EEG to identify critical events that are worth of inspection for diagnostic purposes. A novel...
In order to predict short-term times series with incomplete data, a proposed approach is presented based on the energy associated of series. A benchmark of rainfall time series and Mackay Glass (MG) samples are used. An average smoothing technique is adopted to complete the dataset. The structure of the predictor filter is changed taking into account the energy associated of the short series. The...
Intention understanding is a basic requirement for human-machine interaction. Action classification and object affordance recognition are two possible ways to understand human intention. In this study, Multiple Timescale Recurrent Neural Network (MTRNN) is adapted to analyze human action. Supervised MTRNN, which is an extension of Continuous Timescale Recurrent Neural Network (CTRNN), is used for...
Radial Basis Functions Neural Network (RBFNN) as the outcome of recent research provides a simple model for complex networks. This is achieved by employing the Radial Basis Function (RBF) in the network as hidden neuron patterns. The optimal properties of the RBFs pave the way for stable approximation. However, it is generally rather difficult to determine the locations of the centers and the shape...
A computing architecture based on neuronal principles is presented, which implements learning to reach towards visually-perceived targets for an embodied agent. The whole behavioural loop from object perception to motor control is realised in the architecture using attractor dynamics and Dynamic Neural Fields. The sensory-motor mappings, involved in generation of saccadic gaze shifts and goal-directed...
The analysis of electroencephalogram (EEG) signal is a low-cost and effective technique to examine electrical activity of the brain and diagnose brain diseases in the Brain Computer Interface (BCI) applications. Classification of EEG signals is an important task in BCI applications. This paper investigates two common methods of feature extraction on EEG signals, autoregressive (AR) model and approximate...
The entorhino-hippocampal neural circuits of the mammalian brains are able to internally generate efficient spatial representations of large-scale environments. Hippocampal principal cells in mammalian brains form sparse codes of the positions in large environments. The underlying computational mechanisms of the formation of spatial memory in large-scale environments are still not well understood...
Manifold Learning methods aim to find meaningful low-dimensional structures hidden in their high-dimensional observations. Recently, they are faced with critical problems of how to reduce computational and space complexity in big data applications, how to determine neighborhood size adaptive to different data sets and how to deal with new observations in an out-of-sample mode. This paper presents...
The aim of this paper is to evaluate the effectiveness of a class of data-driven physical models to represent both acoustic and high-speed video data of the voice production process. Voice production analysis through numerical models of the phonation process is nowday a mature research field, and reliable dynamical glottal models of different accuracy and complexity are available. Although they are...
This paper compares two approaches for predicting air temperature from historical pressure, humidity, and temperature data gathered from meteorological sensors in Northwestern Nevada. We describe our data and our representation and compare a standard neural network against a deep learning network. Our empirical results indicate that a deep neural network with Stacked Denoising Auto-Encoders (SDAE)...
Feature descriptors involved in video processing are generally high dimensional in nature. Even though the extracted features are high dimensional, many a times the task at hand depends only on a small subset of these features. For example, if two actions like running and walking have to be identified, extracting features related to the leg movement of the person is enough. Since, this subset is not...
In the multilayer perceptron (MLP), there was a theorem about the maximum number of separable regions (M) given the number of hidden nodes (H) in the input d-dimensional space. We propose a recurrence relation in the high dimensional space and prove the theorem using the expansion of recurrence relation instead of proof by induction. The MLP model has input layer, one hidden layer, and output layer...
This paper presents a new strategy to build multi tree hierarchical structure SVM which can get a more efficient and accuracy classification model for multiclass problems. Base on the theory of Binary Tree SVM (BTS), we proposed an improvement algorithm which extend binary tree structure to a multi tree structure, In the multi tree hierarchical structure, similarity clustering method was proposed...
We propose an optimization method for belief propagation. First we mathematically show that the belief propagation algorithm can be optimized by imposing a reasonable restriction on the conditional probability tables in a Bayesian network. Then we demonstrate the efficiency of the proposed algorithm with experiments. Compared to the previously derived approximate algorithm, the proposed algorithm...
Kernel methods provide an efficient nonparametric model to produce adaptive nonlinear filtering (ANF) algorithms. However, in practical applications, standard squared error based kernel methods suffer from two main issues: (1) a constant step size is used, which degrades the algorithm performance in non-stationary environment, and (2) additive noises are assumed to follow Gaussian distribution, while...
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