Searching for optimal setting conditions in technological processes using parametric estimation models and neural network mapping approach: A tutorial

The paper describes the architecture of a Spiking Neural Network (SNN) for time waveform analyses using edge computing. The network model was based on the principles of preprocessing signals in the diencephalon and using tonic spiking and inhibition-induced spiking models typical for the thalamus area. The research focused on a significant reduction of the complexity of the SNN algorithm by eliminating most synaptic connections and ensuring zero dispersion of weight values concerning connections between neuron layers. The paper describes a network mapping and learning algorithm, in which the number of variables in the learning process is linearly dependent on the size of the patterns. The works included testing the stability of the accuracy parameter for various network sizes. The described approach used the ability of spiking neurons to process currents of less than 100 pA, typical of amperometric techniques. An example of a practical application is an analysis of vesicle fusion signals using an amperometric system based on Carbon NanoTube (CNT) sensors. The paper concludes with a discussion of the costs of implementing the network as a semiconductor structure.

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Explore Deep Neural Network and Reinforcement Learning to Large-scale Tasks Processing in Big Data

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001419510108 ◽

2019 ◽

Vol 33 (13) ◽

pp. 1951010 ◽

Cited By ~ 1

Author(s):

Chunyi Wu ◽

Gaochao Xu ◽

Yan Ding ◽

Jia Zhao

Keyword(s):

Neural Network ◽

Big Data ◽

Reinforcement Learning ◽

Large Scale ◽

Policy Network ◽

Text Learning ◽

Mapping Algorithm ◽

Task Requirements ◽

Network Mapping

Large-scale tasks processing based on cloud computing has become crucial to big data analysis and disposal in recent years. Most previous work, generally, utilize the conventional methods and architectures for general scale tasks to achieve tons of tasks disposing, which is limited by the issues of computing capability, data transmission, etc. Based on this argument, a fat-tree structure-based approach called LTDR (Large-scale Tasks processing using Deep network model and Reinforcement learning) has been proposed in this work. Aiming at exploring the optimal task allocation scheme, a virtual network mapping algorithm based on deep convolutional neural network and [Formula: see text]-learning is presented herein. After feature extraction, we design and implement a policy network to make node mapping decisions. The link mapping scheme can be attained by the designed distributed value-function based reinforcement learning model. Eventually, tasks are allocated onto proper physical nodes and processed efficiently. Experimental results show that LTDR can significantly improve the utilization of physical resources and long-term revenue while satisfying task requirements in big data.

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Building Extraction at Scale Using Convolutional Neural Network: Mapping of the United States

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2018.2835377 ◽

2018 ◽

Vol 11 (8) ◽

pp. 2600-2614 ◽

Cited By ~ 41

Author(s):

Hsiuhan Lexie Yang ◽

Jiangye Yuan ◽

Dalton Lunga ◽

Melanie Laverdiere ◽

Amy Rose ◽

...

Keyword(s):

Neural Network ◽

United States ◽

Convolutional Neural Network ◽

The United States ◽

Building Extraction ◽

Network Mapping

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High Precision Adaptive Robust Neural Network Control of a Servo Pneumatic System

Applied Sciences ◽

10.3390/app9173472 ◽

2019 ◽

Vol 9 (17) ◽

pp. 3472 ◽

Cited By ~ 1

Author(s):

Chen ◽

Tao ◽

Liu

Keyword(s):

Neural Network ◽

Adaptive Control ◽

Robust Control ◽

Network Control ◽

Parametric Estimation ◽

Pneumatic Cylinder ◽

Unmodeled Dynamics ◽

Neural Network Control ◽

Tracking Accuracy ◽

Neural Network Controller

In this paper, an adaptive robust neural network controller (ARNNC) is synthesized for a single-rod pneumatic actuator to achieve high tracking accuracy without knowing the bounds of the parameters and disturbances. The ARNNC control framework integrates adaptive control, robust control, and neural network control intelligently. Adaptive control improves the precision of dynamic compensation with parametric estimation, and robust control attenuates the effect of unmodeled dynamics and unknown disturbances. In reality, the unmodeled dynamics of the complicated pneumatic systems and unpredictable disturbances in working conditions affect the tracking precision. However, these cannot be expressed as an exact formula. Therefore, the real-time learning radial basis function (RBF) neural network component is considered for better compensation of unmodeled dynamics, random disturbances, and estimation errors of the adaptive control. Although the bounds of the parameters and disturbances for the pneumatic systems are unknown, the prescribed transient performance and final tracking accuracy of the proposed method can be still achieved with fictitious bounds. Asymptotic tracking performance can be acquired under the provided circumstance. The comparative experiments with a pneumatic cylinder driven by proportional direction valve illustrate the effectiveness of the proposed ARNNC as shown by a high tracking accuracy is achieved.

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Fast non-linear extraction of plasma equilibrium parameters using a neural network mapping

Nuclear Fusion ◽

10.1088/0029-5515/31/7/005 ◽

1991 ◽

Vol 31 (7) ◽

pp. 1291-1300 ◽

Cited By ~ 59

Author(s):

J.B. Lister ◽

H. Schnurrenberger

Keyword(s):

Neural Network ◽

Plasma Equilibrium ◽

Non Linear ◽

Network Mapping

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AN EXPLORATION OF FUNCTIONAL SIZE BASED EFFORT ESTIMATION MODELS

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194011005347 ◽

2011 ◽

Vol 21 (03) ◽

pp. 413-429 ◽

Cited By ~ 1

Author(s):

SECKIN TUNALILAR ◽

ONUR DEMIRORS

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Regression Analysis ◽

Measurement Unit ◽

Effort Estimation ◽

Total Size ◽

Generic Models ◽

Functional Components ◽

Estimation Models ◽

Functional Size

A number of methods have been proposed to build a relationship between effort and size. These models are generally based on regression analysis and a widely accepted model is not yet available. Although in some sizing methods, such as MKII and IFPUG, different multipliers for the base functional components (BFC) exist, their origin and the purpose of their usage are undefined. The COSMIC method does not treat components separately and assigns the same measurement unit to each of them. In this study we used the Artificial Neural Network and regression based methods to create effort estimation models that take the four components of the COSMIC method into consideration. In the research we compared several functional size based effort models in terms of accuracy using a reliable company dataset. These models comprised not only the generic models proposed in the literature or currently in use, but also specific models that we generated using our dataset with a single and multi-variate regression analysis and the ANN method. We also explored the effect of functional similarity (FS) using our specific models. We found that using BFC instead of total size improved effort estimation models and the ANN method is a useful approach to calibrate these components according to the company characteristics.

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