APPLICATION OF NEURAL NETWORKS AND EVOLUTIONARY ALGORITHMS TO SOLVE ENERGY OPTIMIZATION AND UNIT COMMITMENT FOR A SMART CITY

Neural Network World ◽

10.14311/nnw.2018.28.022 ◽

2018 ◽

Vol 28 (4) ◽

pp. 379-413 ◽

Author(s):

Bohumír Garlík

Keyword(s):

Neural Networks ◽

Evolutionary Algorithms ◽

Unit Commitment ◽

Energy Optimization

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A novel movable partitions approach with neural networks and evolutionary algorithms for solving the hydroelectric unit commitment problem

Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference - GECCO '13 ◽

10.1145/2463372.2463523 ◽

2013 ◽

Author(s):

Pedro de Lima Abrão ◽

Elizabeth Fialho Wanner ◽

Paulo Eduardo Maciel de Almeida

Keyword(s):

Neural Networks ◽

Evolutionary Algorithms ◽

Unit Commitment ◽

Unit Commitment Problem ◽

Hydroelectric Unit ◽

Commitment Problem

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Automated Test Input Generation for Convolutional Neural Networks by Implementing Multi-objective Evolutionary Algorithms

2020 Eighth International Symposium on Computing and Networking Workshops (CANDARW) ◽

10.1109/candarw51189.2020.00040 ◽

2020 ◽

Author(s):

Lingfeng ZHANG ◽

Hiroyuki SATO

Keyword(s):

Neural Networks ◽

Evolutionary Algorithms ◽

Convolutional Neural Networks ◽

Multi Objective ◽

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An expert system for unit commitment and power demand prediction using fuzzy logic and neural networks

Expert Systems ◽

10.1111/j.1468-0394.1996.tb00281.x ◽

1996 ◽

Vol 13 (1) ◽

pp. 29-40 ◽

Author(s):

Sameer Bataineh ◽

Adnan Al-Anbuky ◽

Salem Al-Aqtash

Keyword(s):

Neural Networks ◽

Fuzzy Logic ◽

Expert System ◽

Unit Commitment ◽

Power Demand ◽

Demand Prediction

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Case studies in applying fitness distributions in evolutionary algorithms. II. Comparing the improvements from crossover and Gaussian mutation on simple neural networks

2000 IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks. Proceedings of the First IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks (Cat. No.00EX448) ◽

10.1109/ecnn.2000.886224 ◽

2002 ◽

Author(s):

A. Jain ◽

D.B. Fogel

Keyword(s):

Neural Networks ◽

Evolutionary Algorithms ◽

Case Studies ◽

Gaussian Mutation

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Energy optimization in the process industries: Unit Commitment at systems level

Computer Aided Chemical Engineering - 20th European Symposium on Computer Aided Process Engineering ◽

10.1016/s1570-7946(10)28156-7 ◽

2010 ◽

pp. 931-936 ◽

Author(s):

Edwin Zondervan ◽

Ignacio E. Grossmann ◽

André B. de Haan

Keyword(s):

Unit Commitment ◽

Energy Optimization ◽

Process Industries

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Designing Radial Basis Function Neural Networks with Meta-Evolutionary Algorithms: The Effect of Chromosome Codification

Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living - Lecture Notes in Computer Science ◽

10.1007/978-3-642-02481-8_6 ◽

2009 ◽

pp. 37-40

Author(s):

Elisabet Parras-Gutierrez ◽

Victor M. Rivas ◽

M. Jose del Jesus ◽

Juan J. Merelo

Keyword(s):

Neural Networks ◽

Evolutionary Algorithms ◽

Radial Basis Function ◽

Basis Function ◽

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Using Particle Swarm Optimization Algorithm as an Optimization Tool Within Developed Neural Networks

Critical Developments and Applications of Swarm Intelligence - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-5134-8.ch009 ◽

2018 ◽

pp. 215-244

Author(s):

Goran Klepac

Keyword(s):

Neural Network ◽

Neural Networks ◽

Particle Swarm Optimization ◽

Evolutionary Algorithms ◽

Optimization Algorithm ◽

Recommendation System ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Optimal Combination ◽

Swarm Optimization

Developed neural networks as an output could have numerous potential outputs caused by numerous combinations of input values. When we are in position to find optimal combination of input values for achieving specific output value within neural network model it is not a trivial task. This request comes from profiling purposes if, for example, neural network gives information of specific profile regarding input or recommendation system realized by neural networks, etc. Utilizing evolutionary algorithms like particle swarm optimization algorithm, which will be illustrated in this chapter, can solve these problems.

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A Perfect Integration of Neural Networks and Evolutionary Algorithms

Artificial Neural Nets and Genetic Algorithms ◽

10.1007/978-3-7091-7535-4_25 ◽

1995 ◽

pp. 88-91 ◽

Author(s):

Percy P. C. Yip ◽

Yoh-Han Pao

Keyword(s):

Neural Networks ◽

Evolutionary Algorithms

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Artificial Neural Networks Optimization by means of Evolutionary Algorithms

Soft Computing in Engineering Design and Manufacturing ◽

10.1007/978-1-4471-0427-8_1 ◽

1998 ◽

pp. 3-12 ◽

Author(s):

I. De Falco ◽

A. Della Cioppa ◽

P. Natale ◽

E. Tarantino

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Evolutionary Algorithms ◽

Artificial Neural ◽

Neural Networks Optimization

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Neural Networks and Evolutionary Algorithms for the Prediction of Thermodynamic Properties for Chemical Engineering

Lecture Notes in Computer Science - Simulated Evolution and Learning ◽

10.1007/3-540-48873-1_15 ◽

1999 ◽

pp. 106-113

Author(s):

Martin Mandischer ◽

Hannes Geyer ◽

Peter Ulbig

Keyword(s):

Neural Networks ◽

Thermodynamic Properties ◽

Evolutionary Algorithms ◽

Chemical Engineering

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