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Trigger detection plays a key role in the extraction of biomedical events, so it will influence the results of biomedical events extraction directly. The traditional biomedical event trigger recognition method is based on artificial design features and construct feature vectors; Not only does it consume great amounts of manpower, it also lacks system generalization ability. Most of methods of trigger...
In this paper, we take the average impact value method as the evaluation of neural network variable correlation indicators, analysis the data provided by Professor P. Cortez and A. Morais from University of Minho (Portugal) using the MIVBP algorithm to filtrate 13 characterization factors to get 7 characterization parameters affect forest fires, construct the simulation model of the prediction of...
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...
The main purpose of the research is an assessment of utilization efficiency of artificial intelligence methods for an assessment of effectiveness and feasibility of scientific and technical decisions and technologies for the purpose of implementation of the learning and plug-and-play information analysis system capable of suggesting alternatives of efficiency assessment concerning scientific and technical...
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.
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...
Deep convolutional neural networks (CNN) brought revolution without any doubt to various challenging tasks, mainly in computer vision. However, their model designing still requires attention to reduce number of learnable parameters, with no meaningful reduction in performance. In this paper we investigate to what extend CNN may take advantage of pyramid structure typical of biological neurons. A generalized...
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...
The problems of predicting the Protein-Protein Interactions (PPIs) are characterized by probabilistic constraints using the artificial neural network techniques. In the literature, no specific rules are proposed for determining whether two proteins interact, but various approaches have been proposed to collect the information about the interaction between the proteins. The need and importance of PPIs,...
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...
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...
This paper aims to present a comparison between probabilistic and deterministic spiking neural network for a back Propagation classification algorithm. To have a fair comparison, neuron models and structures are considered identical in both of the networks. The networks are trained and tested with the Iris database. According to the simulation results, the probabilistic network converges faster than...
We present a new application for ensembles on the task of visualisation. Ensemble methods are known for warding off overfitting in learning tasks. The task of interest in this work is visualisation via dimensionality reduction: we take the view that each high-dimensional data item is the image, under a smooth mapping, of a two-dimensional latent coordinate. Learning the mapping from latent to data...
Nowadays, biopolymer has been actively used in two important areas in our daily activities; packaging and medical devices. One of the important criteria in production of biopolymer is the quality of the polymer. Therefore, a method of controlling biopolymer quality (i.e. molecular weight) is certainly indispensable in this matter. Moreover, biopolymerization is a nonlinear process that requires a...
This paper presents AFCMAC, an Auto-adaptive Fuzzy Cerebellar Model Articulation Controller, and its comparison with the traditional CMAC (Cerebellar Model Articulation Controller) for horizontal voluntary eye movements. We evaluated the performance of the AFCMAC and the traditional CMAC, by using the standard deviation of binocular fixation disparity of five healthy control and five dyslexic subjects,...
Aiming at time-varying and complex nonlinear characteristics in the wood drying process, a double hidden process neural network was introduced into wood drying modeling. Time-varying and complex nonlinear characteristics of wood drying were considered adequately in wood drying model by time series data of main influence factors fitted to time-varying functions as network input. The concrete modeling...
Stock price fluctuation in stock markets is a very important issue in financial researches. However, the information in stock markets of China is too much to analysis. Fractal theory is an important modern branch of nonlinear science. Neural network has a strong nonlinear approximation ability and self-organizing, adaptive features. Based on fractal theory, the Shanghai integrated index are chosen...
We create a spiking neural network of Integrate and Fire neurons with spike frequency adaption based on parameters adjusted for our e-nose device, and investigate the use of this model for odor classification. Addition of spike frequency adaptation term brings the model closer to the response of the olfactory system. Data from Cyranose 320, a polymer based 32-sensor array, is used to test the system...
Computer simulation study of brain neuronal networks is an active academic field. Deep Belief Network (DBN) introduces an effective way of training deep neural networks and the Adaptive Resonance Theory (ART) puts forward a two-layer competitive network emulating human cognitive processes. In our study, we implement a DBN with the mechanism of ART which benefits from DBN's multi-layer structure and...
Recently, spiking neural networks (SNNs) have been shown capable of approximating the dynamics of biological neuronal networks, and of being trainable by biologically-plausible learning mechanisms, such as spike-timing-dependent synaptic plasticity. Numerical simulations also support the possibility that they may possess universal function approximation abilities. However the effectiveness of training...
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