scholarly journals Multistage Dynamic Optimization with Different Forms of Neural-State Constraints to Avoid Many Object Collisions Based on Radar Remote Sensing

2020 ◽  
Vol 12 (6) ◽  
pp. 1020
Author(s):  
Józef Lisowski
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
State Constraints ◽  
Optimization Methods ◽  
Simulation Studies ◽  
Radar Remote Sensing ◽  
Optimal Value ◽  
Different Shapes ◽  
Artificial Neural ◽  
Method Of Dynamic Programming

This article presents the possibility of helping navigators direct the movement of an object, while safely passing through other objects, using an artificial neural network and optimization methods. It has been shown that the best trajectory of an object in terms of optimality and security, from among many possible options, can be determined by the method of dynamic programming with the simultaneous use of an artificial neural network, by depicting the encountered objects as moving in forbidden domains. Analytical considerations are illustrated with examples of simulation studies of the developed calculation program on real navigational situations at sea. This research took into account both the number of objects encountered and the different shapes of domains assigned to the objects encountered. Finally, the optimal value of the safe object trajectory time was compared on the setpoint value of the safe passing distance of objects in given visibility conditions at sea, and the degree of discretization of calculations was determined by the density of the location of nodes along the route of objects.

scholarly journals Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

2017 ◽  
Author(s):  
Atul Anand ◽  
L Suganthi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tamil Nadu ◽  
Consumer Price Index ◽  
Optimization Methods ◽  
Electricity Demand ◽  
The State ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Optimizing Algorithm

In  the present study, a hybrid  optimizing algorithm has been proposed using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of  electricity demand of  the state of Tamil Nadu in India. The GA-PSO model optimizes  the coefficients of factors of  gross state domestic product (GSDP), per capita demand, income and  consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015 , the simulation results of GA-PSO models  are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as  ANN-BP, ANN-GA, ANN-PSO models. Further  the paper also forecasts the electricity demand of Tamil Nadu  based on two scenarios. First scenario is the "as-it-is" scenario , the second scenario  is based on milestones set for achieving goals of "Vision 2023" document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that the direct causality exists between  GSDP and the electricity demand of the state.

scholarly journals Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

2018 ◽  
Author(s):  
Atul Anand ◽  
L Suganthi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tamil Nadu ◽  
Consumer Price Index ◽  
Electricity Consumption ◽  
Optimization Methods ◽  
Electricity Demand ◽  
The State ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In the present study, a hybrid optimizing algorithm has been proposed using Genetic Algorithm (GA)and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of electricity demand of the state of Tamil Nadu in India. The GA-PSO model optimizes the coefficients of factors of gross state domestic product (GSDP) , electricity consumption per capita, income growth rate and consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015 , the simulation results of GA-PSO models are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as ANN-BP, ANN-GA, ANN-PSO models. Further the paper also forecasts the electricity demand of Tamil Nadu based on two scenarios. First scenario is the "as-it-is" scenario , the second scenario is based on milestones set for achieving goals of "Vision 2023" document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that a direct causality exists between GSDP and the electricity demand of the state.

scholarly journals Artificial Neural Network Algorithms for 3D Printing

Materials ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 163
Author(s):  
Muhammad Arif Mahmood ◽  
Anita Ioana Visan ◽  
Carmen Ristoscu ◽  
Ion N. Mihailescu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
3D Printing ◽  
Process Parameters ◽  
Optimization Methods ◽  
Physical Models ◽  
Network Algorithms ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Additive manufacturing with an emphasis on 3D printing has recently become popular due to its exceptional advantages over conventional manufacturing processes. However, 3D printing process parameters are challenging to optimize, as they influence the properties and usage time of printed parts. Therefore, it is a complex task to develop a correlation between process parameters and printed parts’ properties via traditional optimization methods. A machine-learning technique was recently validated to carry out intricate pattern identification and develop a deterministic relationship, eliminating the need to develop and solve physical models. In machine learning, artificial neural network (ANN) is the most widely utilized model, owing to its capability to solve large datasets and strong computational supremacy. This study compiles the advancement of ANN in several aspects of 3D printing. Challenges while applying ANN in 3D printing and their potential solutions are indicated. Finally, upcoming trends for the application of ANN in 3D printing are projected.

Search for Optimum Operating Conditions for a Water Purification Process Integrated to a Heat Transformer with Energy Recycling using Artificial Neural Network Inverse Solved by Genetic and Particle Swarm Algorithms

2012 ◽  
Vol 7 (1) ◽  
Author(s):  
Youness El Hamzaoui ◽  
Bassam Ali ◽  
J. Alfredo Hernandez ◽  
Obed Cortez Aburto ◽  
Outmane Oubram
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Purification ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Operating Conditions ◽  
Optimum Operating ◽  
Purification Process ◽  
Artificial Neural ◽  
Heat Transformer

The coefficient of performance (COP) for a water purification process integrated to an absorption heat transformer with energy recycling was optimized using the artificial intelligence. The objective of this paper is to develop an integrated approach using artificial neural network inverse (ANNi) coupling with optimization methods: genetic algorithms (GAs) and particle swarm algorithm (PSA). Therefore, ANNi was solved by these optimization methods to estimate the optimal input variables when a COP is required. The paper adopts two cases studies to accomplish the comparative study. The results illustrate that the GAs outperforms the PSA. Finally, the study shows that the GAs based on ANNi is a better optimization method for control on-line the performance of the system, and constitutes a very promising framework for finding a set of “good solutions”.

Application of Artificial Neural Network Based on GA-BP Algorithm to Predicting Mechanical Properties of TC4

2010 ◽  
Vol 146-147 ◽  
pp. 1698-1701
Author(s):  
Zhe Zhe Hou ◽  
Yan Liang Du
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Heat Treatment ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Optimization Methods ◽  
Calculation Results ◽  
Tc4 Alloy ◽  
Artificial Neural

On the basis of numerous experimental results the effect of heat treatment on mechanical properties of TC4 alloy is studied. A computer model expressing the relationships between heat treatment and mechanical properties has been established with a back propagation feed forword artificial neural network method. The optimization methods based on artificial neural network and the genetic algorithm, using binary system, optimize the weight and threshold by the genetic algorithm. The calculation results show that the model has good learning precision and generalization and it can be used for predicting the mechanical properties of TC4 alloy.

scholarly journals Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

2018 ◽  
Author(s):  
Atul Anand ◽  
L Suganthi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tamil Nadu ◽  
Consumer Price Index ◽  
Electricity Consumption ◽  
Optimization Methods ◽  
Electricity Demand ◽  
The State ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In the present study, a hybrid optimizing algorithm has been proposed using Genetic Algorithm (GA)and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of electricity demand of the state of Tamil Nadu in India. The GA-PSO model optimizes the coefficients of factors of gross state domestic product (GSDP) , electricity consumption per capita, income growth rate and consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015, the simulation results of GA-PSO models are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as ANN-BP, ANN-GA, ANN-PSO models. Further the paper also forecasts the electricity demand of Tamil Nadu based on two scenarios. First scenario is the “as-it-is” scenario, the second scenario is based on milestones set for achieving goals of “Vision 2023” document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that a direct causality exists between GSDP and the electricity demand of the state.

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