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Deep Learning methods have proven to be very successful in classifying large data sets of high feature dimensionality. However, their success usually implies very long training times. In this paper we examine learning methods combining the Random Neural Network, a biologically inspired neural network and the Extreme Learning Machine that achieve state of the art classification performance while requiring...
Biometrie systems present some important advantages over the traditional knowledge-or possess-oriented identification systems, such as a guarantee of authenticity and convenience. However, due to their widespread usage in our society and despite the difficulty in attacking them, nowadays criminals are already developing techniques to simulate physical, physiological and behavioral traits of valid...
The article discusses spiking neural networks, their uniqueness, their ability to training, architecture, and the possibility of a hardware implementation. Special attention is given to reveal the prospects for the development and application of spiking neural networks for the implementation in robotics and control systems.
Recently, more neuroscience researches focus on the role of dendritic structure during the neural computation. Inspired by the specified topologies of numerous dendritic trees, we proposed a single neural model with a particular dendritic structure. The dendrites are composed of several branches, and these branches correspond to three distributions in coordinate, which are used to classify the training...
System identification is the process of developing a mathematical model of a system using input and output knowledge of system. Identification of nonlinear system is well known problem due to its unpredictability and complexity. The nonlinear system for identification is Inverted Pendulum in this work which is well known benchmark system in control system theory due to it's highly nonlinear and unstable...
This paper presents a structural organization of declarative memories using a new model of spiking neurons. Using this model we propose a self-organizing mechanism to build episodic and semantic memories on the cognitive level. Neurons in this approach represent symbolic concepts that are stored and associated with each other based on the observed events in the environment. We demonstrate that the...
For many years, neural networks have gained gigantic interest and their popularity is likely to continue because of the success stories of deep learning. Nonetheless, their applications are mostly limited to static and not temporal patterns. In this paper, we apply time warping invariant Echo State Networks (ESNs) to time-series classification tasks using datasets from various studies in the UCR archive...
The paper presents selected results of research on modeling and modeling identification nerone's development of the electricity market. For example on the electricity market shows the results of neural and identification modeling. As a result of comparative studies showing that yields better results neural modelling than identification modelling for this type of systems containing a large number of...
In a competitive electricity market, an accurate forecasting of energy prices is an important activity for all the market participants. This paper proposes a novel approach based on Neural Networks for forecasting energy prices. Two different architectures of Neural Networks are used. In particular, Multi-Layer Perceptron (MLP) and Fully Connected Neural (FCN) networks are designed, calibrated and...
Software effort estimation is very crucial and there is always a need to improve its accuracy as much as possible. Several estimation techniques have been developed in this regard and it is difficult to determine which model gives more accurate estimation on which dataset. Among all proposed methods, the Radial Basis Function Neural (RBFN) networks models have presented promising results in software...
Software effort estimation is very crucial and there is always a need to improve its accuracy as much as possible. Several estimation techniques have been developed in this regard and it is difficult to determine which model gives more accurate estimation on which dataset. Among all proposed methods, the Radial Basis Function Neural (RBFN) networks models have presented promising results in software...
This paper addresses an investigation regarding the suitability of two different techniques, Active Basis Model (ABM) and Gabor based Convolutional Neural Network (CNN or G-ConvNets) in the mechanism for recognition of biological movement (mammalian visual system model). This method inspired by ventral streams which provide the form information. Both of these approaches contain information of the...
One of the major concerns in Wastewater Treatment Plant (WWTP) operation is that of satisfying the legal requirements that impose maximum allowable concentration levels for effluent pollutants. Not meeting these requirements may generate economic punishment in terms of fines in addition, of course, to the environmental consequences. The effluent limit violations is usually measured as a side performance...
This paper presents a new approach for shortterm load forecasting using the participatory learning paradigm. Participatory learning paradigm is a new training procedure that follows the human learning mechanism adopting an acceptance mechanism to determine which observation is used based upon its compatibility with the current beliefs. Here, participatory learning is used to train a class of hybrid...
This paper proposes a neurobiology-based extension of integrate-and-fire models of Radial Basis Function Neural Networks (RBFNN) that adapts to novel stimuli by means of dynamic restructuring of the network's structural parameters. The new architecture automatically balances synapses modulation, re-centers hidden Radial Basis Functions (RBFs), and stochastically shifts parameter-space decision planes...
Neurons in primary visual cortex (VI) optimally respond to stimuli with their preferred orientation. The response of neurons in VI is suppressed by iso-oriented neurons located in their surround. It is very important to understand the circuitry of center-surround interactions. Previous studies in this field followed the approach of postulating models inspired by neuroscience data. While previous models...
Data centers as a cost-effective infrastructure for hosting Cloud and Grid applications incur tremendous energy cost and C02 emissions in terms of power distribution and cooling. One of the effective approaches for saving energy in a cluster environment is workload consolidation. However, it is challenging to address this schedule problem as it requires the understanding of various cost factors. One...
The purpose of analyzing gene network structure is to identify and understand some unknown related functions and the regulatory mechanisms at molecular level in organisms. Traditional model of the gene regulatory networks often lack an effective method of solving with gene expression profiling data because of high time and space complexity. In this study, a new model of gene regulatory network based...
We propose a novel biologically plausible actor-critic algorithm using policy gradients in order to achieve practical, model-free reinforcement learning. It does not rely on backpropagation and is the first neural actor-critic relying only on locally available information. We show it has an advantage over pure policy gradients methods for motor learning performance in the polecart problem. We are...
Effective monitoring the growth state of seawater phytoplankton plays an important role for the early warning of marine disasters, such as coastal red tides. Grey correlation analysis method was used to select the secondary variables of the soft sensing model. It can effectively reduce the dimension of the system. Extreme learning machine regression (ELMR) method was used to build the soft sensing...
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