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In this paper, an extraction and classification of steady state-visual evoked potentials using the IIR Chebyshev I of 4 order and the adaptive feed-forward Neural Networks algorithm, respectively are applied. The classification results of the extracted signals is directly used to make a user able of controlling the directions (stop, forward, right, and left with stimuli frequencies of 7.5, 10, 15,...
In this study, we investigate the control performance of an adaptive-type feedforward feedback controller using multilayer hypercomplex-valued neural network. The control system consists of a neural network and a feedback controller, whereby the control input of a plant is synthesised online by using the sum of the multilayer hypercomplex-valued neural network and the feedback controller to track...
In order to avoid the serious loss caused by the fire, to achieve the initial fire alarm, multi-sensor system is widely used in fire prediction. In the processing method, it is essentially different from the traditional classical signal. The multi-sensor information fusion system can be merged at different levels. It can be abstracted distributed into three levels: information fusion layer, feature...
With the development of network technology and e-commerce, online-purchasing has become a fashion which takes a significant ratio of the whole market. Product reviews in e-market platform have a lot of information, and buyers tend to rely on the product'information and the reviews to determine the exactly quality of the product. However, the existence of fake reviews will mislead the consumers and...
Examines the use of artificial feedforward neural networks and GRNN type networks in solving prediction problems. Exemplified by student research, the authors conducted the study on the choice of the type of neural network that is most suitable for the solution of such problems. The article desctibes the set-up and the training of generalized regression neural network of and feedforward neural network...
For decades, feedforward neural network (FNN) based classifiers have been extensively used in many classification problems such as image and speech recognition. The inherent parallel nature of the FNN classifier makes it a good candidate for hardware implementations in order to obtain a performance speed up, since most of its computations are matrix-vector multiplications, where the input images arranged...
In this paper, we propose a novel algorithm for compressing neural networks to reduce the memory requirements by using blocked hashing techniques. By adding blocked constraints on top of the conventional hashing technique, the test error rate is maintained while the spatial locality for the computations is preserved. Using this scheme, the synaptic connections are compressed by at least an order (10×)...
Recently, the researchers have been focusing on the convolutional neural network due to its high reliability in image recognition. It is proposed that the feedforward neural network could compete equivalently with the convolutional neural network? In this paper, we have explored the possibility and proposed a feedforward neural network, namely the scaled conjugate gradient backpropagation feedforward...
We introduce a multi-tiered neural network architecture that accurately classifies malignant breast tissue from benign breast tissue. The methodology implemented six different backpropagation neural network (BNN) architectures on 180 malignant and 180 benign breast tissue impedance data files sampled at 47 frequencies from 1 hertz (Hz) to 32 megahertz (MHz). The data were collected utilizing a NovaScan...
Extreme learning machine (ELM) is a learning method for training feedforward neural networks with randomized hidden layer(s). It initializes the weights of hidden neurons in a random manner and determines the output weights in an analytic manner by making use of Moore-Penrose (MP) generalized inverse. No-Prop algorithm is recently proposed training algorithm for feedforward neural networks in which...
Feedforward neural networks are neural networks with (possibly) multiple layers of neurons such that each layer is fully connected to the next one. They have been widely studied in the past partially due to their universal approximation capabilities and empirical effectiveness on a variety of application domains for both regression and classification tasks. In this paper, we provide an overview on...
We present a neural network architecture and a training algorithm designed to enable very rapid training, and that requires low computational processing power, memory and time. The algorithm is based on a modular architecture, which expands the output weights layer constructively, so that the final network can be visualised as a Single Layer Feedforward Network (SLFN) with a large hidden-layer. The...
ELM (extreme learning machine) algorithm has the advantages of fast learning speed, good generalization performance. It is not only suitable for regression, fitting problem, but also applies to the field of classification and pattern recognition. In this paper, ELM algorithm is applied to nonlinear function fitting. The performance and running speed with other algorithms are comparison, show the superiority...
This paper introduces the use of a modified feedforward neural network to cope with the problem of predicting protein functions. Since this kind of classification task is inherently hierarchical, this work proposes the use of two different architectures for the modified feedforward neural network, both mimicking the hierarchical nature of the classes (protein functions) to be predicted. The first...
Krill Herd is a new optimization technique that was inspired by the herding behavior of real small crustaceans called Krills. The method was developed for continuous optimization problems and has recently been successfully applied to different complex problems. Feedforward neural network has a number of characteristics which make it suitable for solving complex classification problems. The training...
Cooperative coevolution is an evolutionary computation method which solves a problem by decomposing it into smaller subcomponents. Multi-objective optimization deals with conflicting objectives and produces multiple optimal solutions instead of a single global optimal solution. In previous work, a multi-objective cooperative co-evolutionary method was introduced for training feedforward neural networks...
Complex-Valued Neural Networks (CVNNs) are Artificial Neural Networks (ANNs) which function using complex numbers - they have complex-valued parameters and accept complex-valued inputs. Phase-Based Neurons (PBNs) are simple CVNNs that use for the internal weights complex numbers with the modulus 1, the only adaptable parameters being the phases of the weights. We present in this paper some limitations...
Presently, all over the world enormous amount of investment are dealing by the Stock Markets. Nationwide financial system are sturdily connected and closely inclined to the accomplishment of their Stock Markets. Additionally nowadays trading has become too reachable capital expenditure medium, for both planned investors as well as common man also. Artificial neural networks (ANN), belonging to Artificial...
In this paper we present a dynamic neural network that dynamically grows the number of the hidden-layer neurons based on an increase in the entropy of the weights during training. The weights are normalized to probability values prior to the computation of the entropy. The entropy being referred is the non-extensive entropy proposed recently by Susan and Hanmandlu for the representation of structured...
It is commonly assumed that neural networks have a built in fault tolerance property mainly due to their parallel structures. Recently the subject was again brought to discussion due to the possibility of using neural networks in nano-electronic systems where fault tolerance and graceful degradation properties would be very important. Neural networks that learn to compute Boolean functions is one...
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