[PDF] Top 20 APPLICATION OF ARTIFICIAL NEURAL NETWORK IN OPTIMIZATION OF SOAP PRODUCTION
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APPLICATION OF ARTIFICIAL NEURAL NETWORK IN OPTIMIZATION OF SOAP PRODUCTION
... According to Kourosh et al (2013) Linear programming includes the optimization of a linear objective function that has a series of limitations in form of linear equality and inequalities. The aim of linear ... See full document
11
A modeling study by artificial neural network on process parameter optimization for silver nanoparticle production
... of production and confirming a sufficient supply of ...the production conditions, such as artificial neural network (ANN) and evolutionary ... See full document
6
Modeling and Optimization of Energy Inputs and Greenhouse Gas Emissions for Eggplant Production Using Artificial Neural Network and Multi-Objective Genetic Algorithm
... farm. Artificial neural networks (ANN) have been widely used in different fields of agriculture like economic, energy and environmental modeling as well as to extend the field of statistical methods, in the ... See full document
12
An artificial neural network model for optimization of finished goods inventory Pages 431-438 Download PDF
... the network acts as a processing element, which performs a weighted sum of all input variables that are fed to the ...the neural network learning is ‘the backward- propagation algorithm’ with scaled ... See full document
8
Applications of Particle Swarm Optimization
... An Artificial Neural Network, often just called a neural network, is a mathematical model inspired by biological neural ...A neural network consists of an ... See full document
5
HAND WRITING RECOGNITION USING HYBRID PARTICLE SWARM OPTIMIZATION & BACK PROPAGATION ALGORITHM
... Artificial Neural Network (ANN) has been around since the late ...recognition. Artificial Neural Network (ANN) is a collection of very simple and massively interconnected ...the ... See full document
7
Application of Artificial Neural Networks in the Prediction of Critical Buckling Loads of Helical Compression Springs
... the network for about 5000 ...a network training function that updates weights and bias values according to the Levenberg- Marquardt (LM) optimization which is the most widely used ... See full document
9
Shape Optimization of Pedestals Using Artificial Neural Network
... and artificial neural networks in a gradientless method of shape ...shape optimization approach for minimizing stress concentration ...for optimization called as ‘curvature function ...years ... See full document
7
OPTIMIZATION OF THE PROCESS CONSTRAINTS IN SPARK EROSION MACHINING OF ALUMINIUM ALLOY AA 6061 HYBRID COMPOSITES USING ARTIFICIAL NEURAL NETWORK
... implement Artificial Neural Network (ANN), to improve spark erosion machining performance of aluminum alloy AA 6061 hybrid composites by controlling the process constraints, which is suitable for bio ... See full document
8
Comparative Study between Neural Network Model and Mathematical Models for Prediction of Glucose Concentration during Enzymatic Hydrolysis
... [5]. Artificial NNs seem to be a feasible alternative in several instances, and their application for biotechnological processes is continuously growing ...glucose production under various enzymatic ... See full document
6
Optimizing Process Parameters of Rotary Furnace using Bio fuels: An Interactive ANN Approach
... using Artificial Neural Networks as an optimization tool solves a very challenging problem of selecting the optimal process output of melting rate for the production of homogenous high quality ... See full document
7
An application of artificial neural network classifier for medical diagnosis
... proposed neural network model, and brief steps on how to design the network model for medical ...the network simulation, (2) the pre-processing procedure which is used to remove the ... See full document
43
Approaches in RSA Cryptosystem Using Artificial Neural Network
... the network performance here, the correlation rate value is considered as where the value is near the integer number 1 or -1 the there is a good prediction of the output and where the value is near zero there is ... See full document
7
Implementation of Artificial Neural Network Training Data in Micro-Controller Based Embedded System
... Matlab still produces the test patterns and then sends them via the serial port to the microcontroller. This simulates data the microcontroller would gain from another source like the analog to digital converter. Once ... See full document
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COMPARATIVE STUDY OF INTELLIGENT TECHNIQUES FOR SOLVING OPF PROBLEM
... Jithendranath, J. ; Babu, B.Y.[23] presents a significant evolutionary based algorithm for solving conventional Optimal Reactive Power Dispatch (ORPD) problem in power system. This problem was designed as a Multi- ... See full document
13
Optimization of EDM Process Parameters by Using Artificial Neural Network: A Review
... Back propagation neural network with GA values compared with the regression models based on the RSM method. Better prediction values were attained by ANN and GA than RSM regression equation. 2013 Agrawal et ... See full document
12
Deep learning : an introduction for applied mathematicians
... physical application imposes natural constraints on one or more of the hidden layers [5, ...the network design—the weights and biases, and hence the tasks performed by each layer, emerge from the training ... See full document
30
Neural Networks in Business Forecasting
... all neural network forecasting ...by neural networks. Some of these application areas include accounting (forecasting accounting earnings, earnings surprises; predicting bankruptcy and ... See full document
15
A Quantitative Structure-activity Relationships
... (CAPSO) is proposed, which is used to molecular descriptors screening and optimization of the 15.. weights of back propagation artificial neural network (BP ANN).[r] ... See full document
11
Diagnosis of Breast Cancer by Combining the Techniques of Data Mining and Artificial Immune System
... the neural network, by calling the Train_Ais function, initially as the network input, the generated network data is taken, then the number of weights and biases in the first layer and the ... See full document
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