Gear Noise Diagnosis System for Automobile Transmission Using Artificial Intelligence (Effect of Number of Intermediate Layers on Judgement Performance)

2013 ◽  
Author(s):  
Morimasa Nakamura ◽  
Masahiko Nishiyama ◽  
Ichiro Moriwaki
Keyword(s):  
Neural Network ◽  
Moment Method ◽  
Network System ◽  
Noise Levels ◽  
Diagnosis System ◽  
Gear Noise ◽  
Intermediate Layers ◽  
The Neural Network ◽  
Neural Network System ◽  
The Moment

The present paper describes a digitizing method for the measured gear noise and a construction of a neural network system for gear noise diagnosis. Gear noise emitted from automobile transmissions should be evaluated by gear noise experts. Although quietness performance estimates from measured noise levels of the transmissions on some production lines, the estimation must be very difficult. There is not a certain relationship between the measured noise levels and the evaluations by the gear noise experts. Therefore, the estimation should be severe. As a result, such an automatic gear noise diagnosis system must yield transmissions with over-quality. The present study deals with a new gear noise diagnosis system to which an artificial intelligence, that is, a neural network system is applied. The previous evaluations by the new gear noise diagnosis system were good when the statistical property of the teacher signals from which the neural network system learned was similar to that of population. This fact means that many teacher signals are necessary on the practical use. Proposed digitizing method of gear noise levels provided good evaluations of neural network system even when the statistical properties of the teacher signals were not similar to that of the population. In addition, a new method, “Moment method” for determining the construction of the neural network system was introduced instead of “Back Propagation Method”. The Moment Method contributed to the improvement of the system judgments. The neural network system constructed using the Moment Method brought good performance. And the number of intermediate layers in the neural network system could be small enough to obtain good performance. It was found that the Moment method provided good learning because of connecting weights update function. When the Moment method was used for determining the connection weights between neurons in the neural network system, the developed gear noise diagnosis system achieved high and stable correct answer ratio. And the number of intermediate layers in the neural network system was only one enough for obtaining good performance of the system. Four intermediate layers, which was the maximum in this paper, did not provide much good performance.

scholarly journals Research on Modeling and Dynamic Characteristics of Complex Biological Neural Network Model considering BP Neural Network Method

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hongyan Chen
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Dynamic Characteristics ◽  
Bp Neural Network ◽  
Data Encryption ◽  
Network System ◽  
Biological Neural Network ◽  
The Neural Network ◽  
Neural Network System

Biological neural network system is a complex nonlinear dynamic system, and research on its dynamics is an important topic at home and abroad. This paper briefly introduces the dynamic characteristics and influencing factors of the neural network system, including the effects of time delay and noise on neural network synchronization, synchronous transition, and stochastic resonance, and introduces the modeling of the neural network system. There are irregular mixing problems in the complex biological neural network system. The BP neural network algorithm can be used to solve more complex dynamic behaviors and can optimize the global search. In order to ensure that the neural network increases the biological characteristics, this paper adjusts the parameters of the BP neural network to receive EEG signals in different states. It can simulate different frequencies and types of brain waves, and it can also carry out a variety of simulations during the operation of the system. Finally, the experimental analysis shows that the complex biological neural network model proposed in this paper has good dynamic characteristics, and the application of this algorithm to data information processing, data encryption, and many other aspects has a bright prospect.

scholarly journals A Modified Neural Network system based on Morphological operations for detection of images with variation in Gray level intensity and facial expressions

2012 ◽  
pp. 56-61
Author(s):  
Pratibha Rani ◽  
Anshu Sirohi ◽  
Manish Kumar Singh
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Network System ◽  
Morphological Operations ◽  
Gray Level ◽  
Back Propagation Algorithm ◽  
Translation Invariant ◽  
The Neural Network ◽  
Neural Network System ◽  
Level Shifts

We introduce an algorithm based on the morphological shared-weight neural network. Which extract the features and then classify them. This type of network can work effectively, even if the gray level intensity and facial expression of the images are varied. The images are processed by a morphological shared weight neural network to detect and extract the features of face images. For the detection of the edges of the image we are using sobel operator. We are using back propagation algorithm for the purpose of learning and training of the neural network system. Being nonlinear and translation-invariant, the morphological operations can be used to create better generalization during face recognition. Feature extraction is performed on grayscale images using hit-miss transforms that are independent of gray-level shifts. The recognition efficiency of this modified network is about 98%.

scholarly journals Thermal Conductivity Modeling of Propylene Glycol - Based Nanofluid Using Artificial Neural Network

2018 ◽  
Vol 14 (1) ◽  
pp. 5281-5291 ◽  
Author(s):  
R. A. Mohamed ◽  
D. M. Habashy
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Propylene Glycol ◽  
Network Model ◽  
Neural Network Model ◽  
Network System ◽  
The Neural Network ◽  
Neural Network System

The article introduces artificial neural network model that simulates and predicts thermal conductivity and particle size of propylene glycol - based nanofluids containing Al2O3 and TiO2 nanoparticles in a temperature rang 20 - 80oc. The experimental data indicated that the nanofluids have excellent stability over the temperature scale of interest and thermal conductivity enhancement for both nanofluid samples. The neural network system was trained on the available experimental data. The system was designed to find the optimal network that has the best training performance. The nonlinear equations which represent the relation between the inputs and output were obtained. The results of neural network model and the theoretical models of the proposed system were performed and compared with the experimental results. The neural network system appears to yield the best fit consistent with experimental data. The results of the paper demonstrate the ability of neural network model as an excellent computational tool in nanofluid field.

scholarly journals Research on the Psychological Distribution Delay of Artificial Neural Network Based on the Analysis of Differential Equation by Inequality Expansion and Contraction Method

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Hong Zhang ◽  
Iyad Katib ◽  
Hafnida. Hasan
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Stability Conditions ◽  
Network System ◽  
Time Lags ◽  
Neural Network Models ◽  
Network Systems ◽  
The Neural Network ◽  
Neural Network System ◽  
The Stability

Abstract This article first introduces neural networks and their characteristics. Based on a comparison of the structure and function of biological neurons and artificial neurons, it focuses on the structure, classification, activation rules, and learning rules of neural network models. Based on the existing literature, this article adds a distributed time lag term of the neural network system. In the actual problem, history has a very important influence on the current change situation, and it is not only at a specific time in the past. It has an impact on the current state change rate. Therefore, based on the existing literature that only has discrete time lags, this paper adds distributed time lags. Such neural network systems can better reflect real-world problems. In this paper, we use three different inequality scaling methods to study the existence, uniqueness, and global asymptotic stability of a class of neural network systems with mixed delays and uncertain parameters. First, using the principle of homeomorphism, a new upper-norm norm is introduced for the correlation matrix of the neural network, and enough conditions for the existence of unique equilibrium points in several neural network systems are given. Under these conditions, the appropriate Lyapunov is used. Krasovskii functional, we prove that the equilibrium point of the neural network system is globally robust and stable. Numerical experiments show that the stability conditions of the neural network system we obtained are feasible, and the conservativeness of the stability conditions of the neural network system is reduced. Finally, some applications and problems of neural network models in psychology are briefly discussed.

Gear Noise Diagnosis System for Automobile Transmission Using Artificial Intelligence: Improvement of Judgement Performance

2000 ◽  
Author(s):  
Ichiro Moriwaki ◽  
Masahiko Nishiyama
Keyword(s):  
Artificial Intelligence ◽  
Moment Method ◽  
Back Propagation ◽  
New Method ◽  
Neural Net ◽  
Noise Levels ◽  
Diagnosis System ◽  
Gear Noise ◽  
Statistical Property ◽  
Safe Side

Abstract Gear noise emitted from transmission units for automobiles should be evaluated by an expert of gear noise. Although on some production lines, quietness performance estimates from measured noise levels of the units, the estimation must be severe. There is no definite relationship between the measured noise levels and the evaluations by the experts. Therefore, for standing on the safe side, the estimation should be severe. As a result, such an automatic gear noise diagnosis system must yield transmission units with over-quality. The present study deals with a new gear noise diagnosis system to which an artificial intelligence is applied; i.e., a neural net is applied. The former evaluations by the new gear noise diagnosis system were quite good when the statistical property of the teacher signals from which the system learned was similar to that of population. This fact means many teacher signals are necessary on the practical use. The present paper describes a new method for digitizing the measured noise levels. This method provided good evaluations of the system even when the statistical property of the teacher signals were not similar to that of the population. In addition, a new method, “Moment Method” for determining the construction of the neural net was introduced instead of “Back Propagation Method”. The Moment. Method contributed to the improvement of the system judgements.

Method for predicting surgical complications

2021 ◽  
pp. 72-79
Author(s):  
Anatoly Solomakha Anatoly Solomakha ◽  
Vladimir Ivanovich Gorbachenko
Keyword(s):  
Neural Network ◽  
Surgical Complications ◽  
Lung Diseases ◽  
Network Models ◽  
High Accuracy ◽  
Network System ◽  
Neural Network Models ◽  
The Neural Network ◽  
Neural Network System ◽  
Accuracy Rates

he problem of predicting the risk of purulent-inflammatory complications after surgery in patients with purulent-destructive lung diseases is still unsolved. When analyzing a sample of 543 patients with purulent-destructive lung diseases in the Penza Regional Clinical Hospital, 45 (8.3 %) had purulent-inflammatory complications. The aim of the study is to create a neural network system for predicting the risk of surgical complications in patients with purulent-destructive lung diseases. As a result of this study, the technology of constructing neural network models for predicting complications in thoracic surgery was developed. In particular: methods of selection and transformation of features have been developed and the neural network system «Neuropredictor» has been developed, which has demonstrated high accuracy rates.

Neural network system for detecting and neutralizing remote unauthorized interference into the components of the Internet of Things

2021 ◽  
Author(s):  
A. I. Vlasov ◽  
E. R. Zakharov ◽  
V. O. Zakharova
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Normal Modes ◽  
The Internet ◽  
Network System ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Neural Network System ◽  
Artificial Neural ◽  
The Internet Of Things

In this work the authors have analyzed the neural network system for detecting and neutralizing remote and unauthorized interference with components of the Internet of Things. The main focus is on considering the neural network approach to detecting intrusions into the Internet of Things network, its monitoring and countering suspicious activity on the host. Features of development of model of artificial neural networks for application of apparatus of neural network in this direction have been considered. This allows you to reflect the successful identification of various types of attacks in terms of true and false positive results. However, the problems of obtaining data on overload and critical modes of the system remain unresolved. The use of a neural network system for detecting and neutralizing remote and unauthorized interference with components of the Internet of Things allows you to implement a module for detecting anomalies in the network, based on the Voltaire series, which considers the theoretical prerequisites of the method of dynamically building an artificial neural network. The main types of attacks, types of intrusion detection systems, interpretations of the obtained data, a brief study of works in the field of neural network solutions have been analyzed. An effective solution has been offered to protect workstations in the Internet of Things network from unauthorized access, and to configure security for all component modules. In conclusion, recommendations have been given for implementing the construction of a neural network module that detects deviations in the operation of the Internet of Things from normal modes.

Continuous Temporal and Spatial Finite Element Boundary Condition Assignments Using a Neural Network Prediction System

2000 ◽  
Author(s):  
Yong Zhang ◽  
John M. Sullivan
Keyword(s):  
Neural Network ◽  
Boundary Condition ◽  
Finite Element ◽  
Time History ◽  
Field Measurements ◽  
Network System ◽  
The Neural Network ◽  
Neural Network System ◽  
Neural Network Prediction ◽  
Temporal And Spatial

Abstract Finite element analyses of multi-dimensional, partial differential equations require accurate and complete boundary condition assignments. However, continuous temporal and spatial values along physical boundaries are rarely available when solving realistic problems based on field data. To mitigate this situation a neural network system was developed and coupled with an interpolation routine. This innovative computational utility was used to provide continuous boundary condition information in time and space along physical boundaries of interest. The neural network was trained to predict the time response at discrete boundary locations based on field measurements at those locations. Once trained the neural network system was capable of providing a continuous time history for those locations. This system was then linked to an interpolation routine which handled the spatial component of the boundary condition specifications. These coupled routines facilitated rapid deployment and testing of various finite element representations. Further, the neural network system captured the transient physics of the situation more accurately than interpolation routines used previously.

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