Neural Syntheses

2021 ◽  
pp. 556-578
Author(s):  
You Nakai
Keyword(s):  
Neural Network ◽  
The Neural Network ◽  
Actual Mechanism ◽  
Custom Made ◽  
Universal Instrument ◽  
Network Instrument

One of Tudor’s last projects used an instrument custom-made for him using the neural network chip that had just been developed. The Neural Synthesizer began as an attempt to build a universal instrument that would synthesize the proliferation of his modular devices. But the actual mechanism of the analog chip, which happened to be an extensive array of amplifiers, shifted the nature of the endeavor, causing a return to the no-input works from the 1970s. In this way, the neural network instrument, used against its usual purpose of extracting patterns from past examples, nonetheless found a strange connection with reminiscences of Tudor’s own past. The analyses of Neural Syntheses and Neural Network Plus, two series of works Tudor made using his new synthesizer, further brings up the issue of memory concerning the performance of his music, which is different every time yet open to revivals, something he tried to capture by setting a number to each performance. This also connects to the problem of how Tudor thought of passing his music on to others so that they could be performed in his absence, a natural concern in the last years of his life, but also something that reflected his lifelong interest in the role of memory and reminiscence in music.

scholarly journals Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering

Metals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1832
Author(s):  
Wojciech Sitek ◽  
Jacek Trzaska
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Vital Role ◽  
Materials Engineering ◽  
The Neural Network ◽  
Critical Issues ◽  
Artificial Neural ◽  
The Individual

Artificial neural networks are an effective and frequently used modelling method in regression and classification tasks in the area of steels and metal alloys. New publications show examples of the use of artificial neural networks in this area, which appear regularly. The paper presents an overview of these publications. Attention was paid to critical issues related to the design of artificial neural networks. There have been presented our suggestions regarding the individual stages of creating and evaluating neural models. Among other things, attention was paid to the vital role of the dataset, which is used to train and test the neural network and its relationship to the artificial neural network topology. Examples of approaches to designing neural networks by other researchers in this area are presented.

scholarly journals Heat Emitting Damage in Skin: A Thermal Pathway for Mechanical Algesia

2021 ◽  
Vol 15 ◽  
Author(s):  
Tom Vincent-Dospital ◽  
Renaud Toussaint ◽  
Knut Jørgen Måløy
Keyword(s):  
Neural Network ◽  
Infrared Camera ◽  
Biological Tissues ◽  
Thermal Dissipation ◽  
Comprehensive Understanding ◽  
Porcine Skin ◽  
Thermal Pain ◽  
The Neural Network ◽  
Lower Temperature

Mechanical pain (or mechanical algesia) can both be a vital mechanism warning us for dangers or an undesired medical symptom important to mitigate. Thus, a comprehensive understanding of the different mechanisms responsible for this type of pain is paramount. In this work, we study the tearing of porcine skin in front of an infrared camera, and show that mechanical injuries in biological tissues can generate enough heat to stimulate the neural network. In particular, we report local temperature elevations of up to 24°C around fast cutaneous ruptures, which shall exceed the threshold of the neural nociceptors usually involved in thermal pain. Slower fractures exhibit lower temperature elevations, and we characterise such dependency to the damaging rate. Overall, we bring experimental evidence of a novel—thermal—pathway for direct mechanical algesia. In addition, the implications of this pathway are discussed for mechanical hyperalgesia, in which a role of the cutaneous thermal sensors has priorly been suspected. We also show that thermal dissipation shall actually account for a significant portion of the total skin's fracture energy, making temperature monitoring an efficient way to detect biological damages.

APPLICATION OF ARTIFICIAL NEURAL NETWORK TO THE IDENTIFICATION OF QUARK AND GLUON JETS

2005 ◽  
Vol 20 (32) ◽  
pp. 7603-7611 ◽  
Author(s):  
MEILING YU ◽  
KUNSHI ZHANG ◽  
LIANSHOU LIU
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Network Architectures ◽  
The Neural Network ◽  
Gluon Jets ◽  
Input Parameters ◽  
Order Of Magnitude ◽  
Two Parameters

The Back-Propagation neural network method is used to identify quark and gluon jets generated by Monte Carlo method. The effects of some factors, such as the architecture of neural network, the input parameters, the training precision and the acceptance cut, on the performance of the neural network are studied in detail. The efficiency and purity of identified quark and gluon jets are calculated for different network architectures. It is found that in order to keep the role of all the input parameters balance, they have to be scaled to the same order of magnitude. Through the study on how the efficiency and purity of the identified quark- and gluon-jets vary with the training precision and acceptance cut, a guidance for how to choose these two parameters is given.

ROLE OF CHAOS IN TRIAL-AND-ERROR PROBLEM SOLVING BY AN ARTIFICIAL NEURAL NETWORK

1996 ◽  
Vol 07 (01) ◽  
pp. 101-108 ◽  
Author(s):  
ICHIRO OBANA ◽  
YASUHIRO FUKUI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Problem Solving ◽  
Motor Neurons ◽  
Neural Activity ◽  
Random Walking ◽  
Trial And Error ◽  
Naturally Occurring ◽  
The Neural Network

One role of chaotic neural activity is illustrated by means of computer simulations of an imaginary agent’s goal-oriented behavior. The agent has a simplified neural network with seven neurons and three legs. The neural network consists of one photosensory neuron and three pairs of inter- and motor neurons. The three legs whose movements are governed by the three motor neurons allow the agent to walk in six concentric radial directions on a plane. It is intended that the neural network causes the agent to walk in a direction of greater brightness, to reach finally the most brightly lit place on the plane. The presence of only one sensory neuron has an important meaning. That is, no immediate information on directions of greater brightness is sensed by the agent. In other words, random walking in the manner of trial-and-error problem solving must be involved in the agent’s walking. Chaotic firing of the motor neurons is intended to play a crucial role in generating the random walking. Brief random walking and rapid straight walking in a direction of greater brightness were observed to occur alternately in the computer simulation. Controlled chaos in naturally occurring neural networks may play a similar role.

scholarly journals Prediksi Tingkat Kelancaran Kredit BSU BMT Tunas Harapan Syari’ah Pringgasela Kabupaten Lombok Timur Menggunakan Algoritma Neural Network

2021 ◽  
Vol 4 (2) ◽  
pp. 205-216
Author(s):  
Amri Muliawan Nur ◽  
◽  
Imam Fathurrahman ◽  
Yahya Yahya ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Data Processing ◽  
Statistical Data ◽  
Network Data ◽  
Mining Method ◽  
The Neural Network ◽  
Using Data ◽  
Data Processing Methods

The role of credit in a cooperative is very important. With the credit can be a source of profit for the cooperative. The cooperative was founded with the aim of prospering its members. One of the advantages is that cooperative members can apply for credit loans. To approve the proposed loan, it is necessary to analyze the credit submitted by the members. This has become one of the difficulties for several cooperatives, one of which is KSU BMT Tunas Harapan Syari'ah which is located in thePringgasela village, Pringgasela District, East Lombok Regency. The problem that often arises is that the analysis conducted is often incorrect, resulting in a prolonged bad credit in installment payments. The reason is that cooperatives always use statistical data which is sometimes inaccurate because there is no processing using data processing methods. Therefore, the neural network data mining method can be used as a tool to analyze which customers are problematic and not problematic. From the results of the research that has been done, it produces an accuracy of 96.19% and an AUC of 0.976

scholarly journals Global Stability, Bifurcation, and Chaos Control in a Delayed Neural Network Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Amitava Kundu ◽  
Pritha Das
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bifurcation Analysis ◽  
Time Delays ◽  
Chaos Control ◽  
Multiple Time ◽  
Multiple Time Delays ◽  
The Neural Network

Conditions for the global asymptotic stability of delayed artificial neural network model of n (≥3) neurons have been derived. For bifurcation analysis with respect to delay we have considered the model with three neurons and used suitable transformation on multiple time delays to reduce it to a system with single delay. Bifurcation analysis is discussed with respect to single delay. Numerical simulations are presented to verify the analytical results. Using numerical simulation, the role of delay and neuronal gain parameter in changing the dynamics of the neural network model has been discussed.

Web Usage Mining by Neural Hybrid Prediction with Markov Chain Components

2021 ◽  
Author(s):  
Arpad Gellert
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Web Browser ◽  
Web Prefetching ◽  
Web Resources ◽  
Web Usage ◽  
The Neural Network ◽  
Prediction Technique ◽  
Web Access

This paper presents and evaluates a two-level web usage prediction technique, consisting of a neural network in the first level and contextual component predictors in the second level. We used Markov chains of different orders as contextual predictors to anticipate the next web access based on specific web access history. The role of the neural network is to decide, based on previous behaviour, whose predictor’s output to use. The predicted web resources are then prefetched into the cache of the browser. In this way, we considerably increase the hit rate of the web browser, which shortens the load times. We have determined the optimal configuration of the proposed hybrid predictor on a real dataset and compared it with other existing web prefetching techniques in terms of prediction accuracy. The best configuration of the proposed neural hybrid method provides an average web access prediction accuracy of 86.95%.

scholarly journals The Role of Geterogeneity in Synchronization of Spiking Neural Networks

2018 ◽  
Vol 13 (2) ◽  
pp. 490-506
Author(s):  
I.E. Mysin ◽  
A.V. Chizhov
Keyword(s):  
Neural Network ◽  
Analytical Modeling ◽  
Monte Carlo Modeling ◽  
Synchronous Mode ◽  
Neural Ensembles ◽  
Exciting Current ◽  
The Neural Network ◽  
Neural Populations ◽  
The Brain

The diversity and heterogeneity of neurons and synapses is an important factor in the functioning of the brain. In our work, we investigated the role of heterogeneity of neural populations in the occurrence of synchronous modes in a network connected by exciting links in the presence of an external exciting input. Using Monte-Carlo modeling and the semi-analytical modeling the distribution of the refractory density of neuron integrators and Hodgkin – Huxley neurons, we showed that there is a range of parameters for the stimulating current and the strength of connections in the population where the effects of neuronal heterogeneity on the threshold or on the stimulating current are opposite. For large values ​​of synaptic weights and subthreshold values ​​of the exciting current, heterogeneity contributes to the emergence of a synchronous mode in the neural network, while at the same time reducing the coupling strength and increasing the exciting current. The heterogeneity reduces the tendency of the neural network to synchronize. The results obtained make it possible to reconcile the known data on the effects of heterogeneity in the regulation of the synchronous regimes arising in the neural ensembles of the brain.

scholarly journals Neural Network Based Identification of Material Model Parameters to Capture Experimental Load-deflection Curve

10.14311/636 ◽  
2004 ◽  
Vol 44 (5-6) ◽  
Author(s):  
D. Novák ◽  
D. Lehký
Keyword(s):  
Neural Network ◽  
High Performance ◽  
High Performance Concrete ◽  
Material Model ◽  
Simulation Method ◽  
Model Parameters ◽  
New Approach ◽  
The Neural Network ◽  
Load Deflection Curve

A new approach is presented for identifying material model parameters. The approach is based on coupling stochastic nonlinear analysis and an artificial neural network. The model parameters play the role of random variables. The Monte Carlo type simulation method is used for training the neural network. The feasibility of the presented approach is demonstrated using examples of high performance concrete for prestressed railway sleepers and an example of a shear wall failure. 

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