scholarly journals A model of parallel sorting neural network of discrete-time

2020 ◽  
Vol 1 (1) ◽  
pp. 67-72
Author(s):  
P. Tymoshchuk
Keyword(s):  
Neural Network ◽  
Discrete Time ◽  
High Speed ◽  
Input Data ◽  
Hardware Implementation ◽  
Unknown Input ◽  
System Of Differential Equations ◽  
Parallel Sorting ◽  
Step Functions ◽  
Finite Resolution

A model of parallel sorting neural network of discrete-time is presented. The model is described by a system of differential equations and by step functions. The network has high speed, any finite resolution of input data and it can process unknown input data of finite values located in arbitrary finite range. The network is characterized by moderate computational complexity and complexity of hardware implementation. The results of computer simulation illustrating the efficiency of the network are provided.

SIMPLIFIED PARALLEL SORTING DISCRETE-TIME NEURAL NETWORK MODEL

2017 ◽  
Vol 2 (1) ◽  
pp. 94-101
Author(s):  
Tymoshchuk. P. ◽  
Keyword(s):  
Neural Network ◽  
Computational Complexity ◽  
Difference Equation ◽  
Discrete Time ◽  
Input Data ◽  
Neural Circuit ◽  
Parallel Sorting ◽  
Step Functions ◽  
Finite Resolution ◽  
Computing Simulation

A model of parallel sorting neural network of discrete-time has been proposed. The model is described by system of difference equations and by step functions. The model is based on simplified neural circuit of discrete-time that identifies maximal/minimal values of input data and is described by difference equation and by step functions. A bound from above on a number of iterations required for reaching convergence of search process to steady state is determined. The model does not need a knowledge of change range of input data. In order to use the model a minimal difference between values of input data should be known. The network can process unknown input data with finite values, located in arbitrary unknown finite range. The network is characterized by moderate computational complexity and complexity of software implementation, any finite resolution of input data, speed,. Computing simulation results illustrating efficiency of the network are given. Keywords — Parallel sorting, neural network, difference equation, computational complexity, hardware implementation.

NONLINEAR INTERPOLATIVE EFFECT OF FEEDBACK TEMPLATE FOR IMAGE PROCESSING BY DISCRETE-TIME CELLULAR NEURAL NETWORK

2003 ◽  
Vol 12 (04) ◽  
pp. 505-518 ◽  
Author(s):  
NOBUAKI TAKAHASHI ◽  
TSUYOSHI OTAKE ◽  
MAMORU TANAKA
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Discrete Time ◽  
Digital Signal Processor ◽  
Wavelet Transforms ◽  
High Speed ◽  
Digital Signal ◽  
Cellular Neural Network ◽  
Discrete Cosine Transformation ◽  
Processing Techniques

Recently a discrete-time cellular neural network (DT-CNN) is applied to many image processing applications such as compression and reconstruction, recognition and so on. Conventional image processing techniques such as the discrete cosine transformation (DCT) and wavelet transforms work as a simple filter and do not make good use of interpolative dynamics by the feedback A template, which is one of the significant characteristics of a cellular neural network (CNN). If CNN is applied to a filter by an only feedforward B template, one should make a model which consists of digital filters using high speed signal processing modules such as a high speed digital signal processor. This paper describes the nonlinear interpolative effect of the feedback A template, by showing the evaluation of image compression and reconstruction.

Design of a High-Speed Digital FIR Filter Based on FPGA

2012 ◽  
Vol 433-440 ◽  
pp. 4571-4577
Author(s):  
Guo Sheng Xu
Keyword(s):  
Digital Filter ◽  
High Speed ◽  
Input Data ◽  
Finite State Machine ◽  
Hardware Implementation ◽  
Simple Structure ◽  
Design Method ◽  
Fir Filter ◽  
Finite State ◽  
Linear And Nonlinear

To realize filtering of high-speed input data, and aiming at the design method of systolic FIR digital filter, this paper proposes a design method of high-speed FIR filter based on FPGA. The states conversion between coefficients configuring mode and filtering mode is finished by FSM (Finite State Machine), which ensures the system to work orderly. The experimental results demonstrated, it can reduce the input dimension and eliminate linear and nonlinear interference effectively. In addition, it is very suitable for hardware implementation due to its simple structure.

scholarly journals Hardware Implementation of Parallelized Fuzzy Adaptive Resonance Theory Neural Network

2020 ◽  
pp. 1-11
Author(s):  
Pavlo Tymoshchuk ◽  
s. Shatny
Keyword(s):  
Neural Network ◽  
Discrete Time ◽  
Continuous Time ◽  
Hardware Implementation ◽  
Adaptive Resonance Theory ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Winner Take All ◽  
Fuzzy Adaptive ◽  
Take All

A hardware implementation design of parallelized fuzzy Adaptive Resonance Theory neural network is described and simulated. Parallel category choice and resonance are implemented in the network. Continuous-time and discrete-time winner-take-all neural circuits identifying the largest of M inputs are used as the winner-take-all units. The continuous-time circuit is described by a state equation with a discontinuous right-hand side. The discrete-time counterpart is governed by a difference equation. Corresponding functional block-diagrams of the circuits include M feed-forward hard- limiting neurons and one feedback neuron, which is used to compute the dynamic shift of inputs. The circuits combine arbitrary finite resolution of inputs, high convergence speed to the winner-take-all operation, low computational and hardware implementation complexity, and independence of initial conditions. The circuits are also used for finding elements of input vector with minimal/maximal values to normalize them in the range [0,1].

A low-cost and high-speed hardware implementation of spiking neural network

2020 ◽  
Vol 382 ◽  
pp. 106-115 ◽  
Author(s):  
Guohe Zhang ◽  
Bing Li ◽  
Jianxing Wu ◽  
Ran Wang ◽  
Yazhu Lan ◽  
...  
Keyword(s):  
Neural Network ◽  
High Speed ◽  
Hardware Implementation ◽  
Low Cost ◽  
Spiking Neural Network

Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

2019 ◽  
Vol 12 (3) ◽  
pp. 248-261
Author(s):  
Baomin Wang ◽  
Xiao Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Rise ◽  
High Speed ◽  
Ball Bearing ◽  
Influence Factors ◽  
Ball Bearings ◽  
Ann Model ◽  
Angular Contact Ball Bearing ◽  
Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

Erratum: High speed opto-electronic neural network

1990 ◽  
Vol 26 (20) ◽  
pp. 1739
Author(s):  
N.M. Barnes ◽  
P. Healey ◽  
P. McKee ◽  
A.W. O'Neill ◽  
M.A.Z. Rejmangreene ◽  
...  
Keyword(s):  
Neural Network ◽  
High Speed

scholarly journals A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 234 ◽  
Author(s):  
Hyun Yoo ◽  
Soyoung Han ◽  
Kyungyong Chung
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Normal Control ◽  
Input Data ◽  
Deep Neural Network ◽  
Pattern Mining ◽  
F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

scholarly journals Influence of hinge length and distribution number on the camber and cornering properties of a non-pneumatic tire

2020 ◽  
Vol 12 (12) ◽  
pp. 168781402098468
Author(s):  
Xianbin Du ◽  
Youqun Zhao ◽  
Yijiang Ma ◽  
Hongxun Fu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Adaptive Learning ◽  
High Speed ◽  
Back Propagation ◽  
Lateral Force ◽  
Learning Ability ◽  
Vehicle Performance ◽  
Neural Network Theory ◽  
Bp Neural Network Model

The camber and cornering properties of the tire directly affect the handling stability of vehicles, especially in emergencies such as high-speed cornering and obstacle avoidance. The structural and load-bearing mode of non-pneumatic mechanical elastic (ME) wheel determine that the mechanical properties of ME wheel will change when different combinations of hinge length and distribution number are adopted. The camber and cornering properties of ME wheel with different hinge lengths and distributions were studied by combining finite element method (FEM) with neural network theory. A ME wheel back propagation (BP) neural network model was established, and the additional momentum method and adaptive learning rate method were utilized to improve BP algorithm. The learning ability and generalization ability of the network model were verified by comparing the output values with the actual input values. The camber and cornering properties of ME wheel were analyzed when the hinge length and distribution changed. The results showed the variation of lateral force and aligning torque of different wheel structures under the combined conditions, and also provided guidance for the matching of wheel and vehicle performance.

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