A New Type of Pulse Neural Network Based on FPGA

2014 ◽  
Vol 556-562 ◽  
pp. 6081-6084
Author(s):  
Qian Huang ◽  
Wen Long Li ◽  
Jian Kang ◽  
Jun Yang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Sigmoid Function ◽  
Nonlinear Excitation ◽  
The Neural Network ◽  
Xor Problem ◽  
Quartus Ii ◽  
New Type ◽  
Hidden Layer ◽  
Eda Tools

In this paper, based on the study analyzed on the basis of a variety of neural networks, a kind of new type pulse neural network is implemented based on the FPGA [1]. The neural network adopts the Sigmoid function as its hidden layer nonlinear excitation function, at the same time, to reduce ROM table storage space and improve the efficiency of look-up table [2], it also adopts the STAM algorithm based nonlinear storage. Choose Altera Corporation’s EDA tools Quartus II as compilation, simulation platform, Cyclone II series EP2C20F484C6 devices and realized the pulse neural networks finally. In the last, we use XOR problem as example to carry out the hardware simulation, and simulation results are consistent with the theoretical value. Neural network to improve the complex, nonlinear, time-varying, uncertainty about the system reliability and security provides a new way.

scholarly journals Using neural networks to describe tracer correlations

2004 ◽  
Vol 4 (1) ◽  
pp. 143-146 ◽  
Author(s):  
D. J. Lary ◽  
M. D. Müller ◽  
H. Y. Mussa
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Chemical Models ◽  
The Neural Network ◽  
Pressure Time ◽  
Fortran Code ◽  
Time Of Year ◽  
Hidden Layer ◽  
And Training

Abstract. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). In this study a neural network using Quickprop learning and one hidden layer with eight nodes was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9995. Such an accurate representation of tracer-tracer correlations allows more use to be made of long-term datasets to constrain chemical models. Such as the dataset from the Halogen Occultation Experiment (HALOE) which has continuously observed CH4  (but not N2O) from 1991 till the present. The neural network Fortran code used is available for download.

scholarly journals Nearly Exponential Neural Networks Approximation in Lp Spaces

2017 ◽  
Vol 26 (1) ◽  
pp. 103-113
Author(s):  
Eman Samir Bhaya ‎ ◽  
Zahraa Mahmoud Fadel
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Continuous Function ◽  
Real Function ◽  
Convex Subset ◽  
Exponential Approximation ◽  
Lp Spaces ◽  
The Neural Network ◽  
Hidden Layer ◽  
Engineering Physics

In different applications, we can widely use the neural network approximation. They are being applied to solve many problems in computer science, engineering, physics, etc. The reason for successful application of neural network approximation is the neural network ability to approximate arbitrary function. In the last 30 years, many papers have been published showing that we can approximate any continuous function defined on a compact subset of the Euclidean spaces of dimensions greater than 1, uniformly using a neural network with one hidden layer. Here we prove that any real function in L_P (C) defined on a compact and convex subset  of can be approximated by a sigmoidal neural network with one hidden layer, that we call nearly exponential approximation.

scholarly journals Neutrosophic Compound Orthogonal Neural Network and Its Applications in Neutrosophic Function Approximation

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 147 ◽  
Author(s):  
Jun Ye ◽  
Wenhua Cui
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Algorithm ◽  
Learning Performance ◽  
Uncertain Information ◽  
Sigmoid Function ◽  
Nonlinear Functions ◽  
Single Output ◽  
Classification Pattern ◽  
Hidden Layer

Neural networks are powerful universal approximation tools. They have been utilized for functions/data approximation, classification, pattern recognition, as well as their various applications. Uncertain or interval values result from the incompleteness of measurements, human observation and estimations in the real world. Thus, a neutrosophic number (NsN) can represent both certain and uncertain information in an indeterminate setting and imply a changeable interval depending on its indeterminate ranges. In NsN settings, however, existing interval neural networks cannot deal with uncertain problems with NsNs. Therefore, this original study proposes a neutrosophic compound orthogonal neural network (NCONN) for the first time, containing the NsN weight values, NsN input and output, and hidden layer neutrosophic neuron functions, to approximate neutrosophic functions/NsN data. In the proposed NCONN model, single input and single output neurons are the transmission notes of NsN data and hidden layer neutrosophic neurons are constructed by the compound functions of both the Chebyshev neutrosophic orthogonal polynomial and the neutrosophic sigmoid function. In addition, illustrative and actual examples are provided to verify the effectiveness and learning performance of the proposed NCONN model for approximating neutrosophic nonlinear functions and NsN data. The contribution of this study is that the proposed NCONN can handle the approximation problems of neutrosophic nonlinear functions and NsN data. However, the main advantage is that the proposed NCONN implies a simple learning algorithm, higher speed learning convergence, and higher learning accuracy in indeterminate/NsN environments.

scholarly journals Development of design system for crack patterns on cup surface based on KANSEI

2018 ◽  
Vol 5 (4) ◽  
pp. 435-441 ◽  
Author(s):  
Mai Misaka ◽  
Hideki Aoyama
Keyword(s):  
Neural Network ◽  
Computer Aided Design ◽  
Back Propagation ◽  
Transition Function ◽  
Design System ◽  
Sigmoid Function ◽  
Crack Patterns ◽  
The Neural Network ◽  
Hidden Layer ◽  
Aided Design

Abstract With the development of manufacturing technology in recent years, as well as with the industrial product development, differentiation in the design aspect is becoming effective, not in terms of performance or quality. In addition, as consumers seek products that match their own sensibilities (KANSEI), designers are required to propose designs that highly conform to concept presented by client, while understanding the KANSEI of diversified consumers; therefore, their burden is increasing. To address these issues, the support of the development of computer-aided design has advanced; however, it is difficult to reflect human KANSEI or to generate a design that induces a natural impression through computers. The purpose of this research is to develop a system that incorporates the KANSEI of users, and emits a pattern design that induces a natural impression using a computer. This work is focused on crack patterns that can be observed on pottery surfaces, and a method for generating crack patterns on a cup surface is suggested. In this study, a Bézier curved surface and fluctuation were employed in order to induce a natural impression. In addition, by using the neural network, the crack patterns were associated with user KANSEI. The neural network was composed of three layers, namely the input layer, the hidden layer, and the output layer; it adopted the sigmoid function as the transition function and the back propagation as the learning method. As a result, a system was constructed, in which a crack pattern that satisfied the input produces an output according to the desired impression of the user. Finally, an evaluation questionnaire was distributed, and the usefulness of the system was confirmed. Highlights A method for creating crack patterns using a computer is proposed. The relationship between KANSEI and crack patterns is modeled by neural network. A system is developed that outputs crack patterns that satisfy the inputted KANSEI.

Contributions to Ranking an Ergonomic Workstation, Considering the Human Effort and the Microclimate Parameters, Using Neural Networks

2013 ◽  
Vol 371 ◽  
pp. 812-816 ◽  
Author(s):  
Daniel Constantin Anghel ◽  
Nadia Belu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Experimental Results ◽  
Work Place ◽  
Feed Forward Neural Network ◽  
Linear Relationships ◽  
The Neural Network ◽  
Manufacture Industry ◽  
Human Effort ◽  
Hidden Layer

The paper presents a method to use a feed forward neural network in order to rank a working place from the manufacture industry. Neural networks excel in gathering difficult non-linear relationships between the inputs and outputs of a system. The neural network is simulated with a simple simulator: SSNN. In this paper, we considered as relevant for a work place ranking, 6 input parameters: temperature, humidity, noise, luminosity, load and frequency. The neural network designed for the study presented in this paper has 6 input neurons, 13 neurons in the hidden layer and 1 neuron in the output layer. We present also some experimental results obtained through simulations.

scholarly journals Using neural networks to describe tracer correlations

2003 ◽  
Vol 3 (6) ◽  
pp. 5711-5724 ◽  
Author(s):  
D. J. Lary ◽  
M .D. Müller ◽  
H. Y. Mussa
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Temporal Dependence ◽  
Chemical Models ◽  
The Neural Network ◽  
Pressure Time ◽  
Fortran Code ◽  
Time Of Year ◽  
Hidden Layer

Abstract. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural 5 network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). In this study a neural network using Quickprop learning and one hidden layer with eight nodes was able to reproduce the CH4-N2O correlation with a correlation co-efficient of 0.9995. Such an accurate representation of tracer-tracer correlations allows more use to be made of long-term datasets to constrain chemical models. Such as the 10 dataset from the Halogen Occultation Experiment (HALOE) which has continuously observed CH4 (but not N2O) from 1991 till the present. The neural network Fortran code used is available for download

scholarly journals Estimation of Approximating Rate for Neural Network inLwpSpaces

2012 ◽  
Vol 2012 ◽  
pp. 1-8
Author(s):  
Jian-Jun Wang ◽  
Chan-Yun Yang ◽  
Jia Jing
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Degree Of Approximation ◽  
Activation Functions ◽  
Feedforward Network ◽  
Topology Construction ◽  
And Topology ◽  
The Neural Network ◽  
Hidden Layer ◽  
Approximation Capability

A class of Soblove type multivariate function is approximated by feedforward network with one hidden layer of sigmoidal units and a linear output. By adopting a set of orthogonal polynomial basis and under certain assumptions for the governing activation functions of the neural network, the upper bound on the degree of approximation can be obtained for the class of Soblove functions. The results obtained are helpful in understanding the approximation capability and topology construction of the sigmoidal neural networks.

Fuzzy Neural Network Based Coal-Rock Interface Recognition

2010 ◽  
Vol 44-47 ◽  
pp. 1402-1406
Author(s):  
Jian Jun Shi ◽  
La Wu Zhou ◽  
Ke Wen Kong ◽  
Yi Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Signal Process ◽  
Network Training ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
Rock Interface ◽  
Coal Rock ◽  
Hidden Layer

. In the coal-rock interface recognition (CIR) technology, signal process and recognition are the key parts. A method for CIR based on BP neural networks and fuzzy technique was proposed in this paper. By using the trail-and-error, the hidden layer dimension of the network was decided. Also the network training and weight modification were studied. In order to get a higher identification ratio, fuzzy neural networks (FNN) based data fusion was studied. For CIR, the structure and algorithm of FNN were determined. The results indicated that the test data can be used to train and simulate with the neural network and FNN. And the proposed method can be used in CIR with a higher recognition ratio.

scholarly journals Construction of neural network classification of Fisher irises based on JavaScript

2019 ◽  
Author(s):  
A.М. Заяц ◽  
С.П. Хабаров
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Back Propagation ◽  
Data Set ◽  
Test Dataset ◽  
Neural Network Classification ◽  
The Neural Network ◽  
Multilayer Neural Networks ◽  
Hidden Layer

Рассматривается процедура выбора структуры и параметров нейронной сети для классификации набора данных, известного как Ирисы Фишера, который включает в себя данные о 150 экземплярах растений трех различных видов. Предложен подход к решению данной задачи без использования дополнительных программных средств и мощных нейросетевых пакетов с использованием только средств стандартного браузера ОС. Это потребовало реализации ряда процедур на JavaScript c их подгрузкой в разработанную интерфейсную HTML-страницу. Исследование большого числа различных структур многослойных нейронных сетей, обучаемых на основе алгоритма обратного распространения ошибки, позволило выбрать для тестового набора данных структуру нейронной сети всего с одним скрытым слоем из трех нейронов. Это существенно упрощает реализацию классификатора Ирисов Фишера, позволяя его оформить в виде загружаемой с сервера HTML-страницы. The procedure for selecting the structure and parameters of the neural network for the classification of a data set known as Iris Fisher, which includes data on 150 plant specimens of three different species, is considered. An approach to solving this problem without using additional software and powerful neural network packages using only the tools of the standard OS browser is proposed. This required the implementation of a number of JavaScript procedures with their loading into the developed HTML interface page. The study of a large number of different structures of multilayer neural networks, trained on the basis of the back-propagation error algorithm, made it possible to choose the structure of a neural network with only one hidden layer of three neurons for a test dataset. This greatly simplifies the implementation of the Fisher Iris classifier, allowing it to be formatted as an HTML page downloaded from the server.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

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