Relevant bibliographies by topics / Evolutionary development of neural network

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Evolutionary development of neural network'

Author: Grafiati
Published: 28 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Evolutionary development of neural network"

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Al-Khowarizmi, Al-Khowarizmi. "Model Classification Of Nominal Value And The Original Of IDR Money By Applying Evolutionary Neural Network." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 3, no. 2 (2020): 258–65. http://dx.doi.org/10.31289/jite.v3i2.3284.

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Indonesian Rupiah (IDR) banknotes have unique characteristics that distinguish them from one another, both in the form of numbers, zeros and background images. This pattern of each type of banknote will be modeled in order to test the nominal value and authenticity of IDR, so as to be able to distinguish not only IDR banknotes but also other denominations. Evolutionary Neural Network is the development of the concept of evolution to get a neural network (NN) using genetic algorithms (GA). In this paper the application of evolutionary neural networks with less input is able to have a better suc
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Li, Xiao Guang. "Research on the Development and Applications of Artificial Neural Networks." Applied Mechanics and Materials 556-562 (May 2014): 6011–14. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.6011.

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Intelligent control is a class of control techniques that use various AI computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic algorithms. In computer science and related fields, artificial neural networks are computational models inspired by animals’ central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected “neurons” that can compute values from inputs by feeding information through the network. Like other
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Xue, Yu, Pengcheng Jiang, Ferrante Neri, and Jiayu Liang. "A Multi-Objective Evolutionary Approach Based on Graph-in-Graph for Neural Architecture Search of Convolutional Neural Networks." International Journal of Neural Systems 31, no. 09 (2021): 2150035. http://dx.doi.org/10.1142/s0129065721500350.

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With the development of deep learning, the design of an appropriate network structure becomes fundamental. In recent years, the successful practice of Neural Architecture Search (NAS) has indicated that an automated design of the network structure can efficiently replace the design performed by human experts. Most NAS algorithms make the assumption that the overall structure of the network is linear and focus solely on accuracy to assess the performance of candidate networks. This paper introduces a novel NAS algorithm based on a multi-objective modeling of the network design problem to design
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Odri, Stevan V., Dusan P. Petrovacki, and Gordana A. Krstonosic. "Evolutional development of a multilevel neural network." Neural Networks 6, no. 4 (1993): 583–95. http://dx.doi.org/10.1016/s0893-6080(05)80061-9.

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LI, KANG, and JIAN-XUN PENG. "SYSTEM ORIENTED NEURAL NETWORKS — PROBLEM FORMULATION, METHODOLOGY AND APPLICATION." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 02 (2006): 143–58. http://dx.doi.org/10.1142/s0218001406004570.

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A novel methodology is proposed for the development of neural network models for complex engineering systems exhibiting nonlinearity. This method performs neural network modeling by first establishing some fundamental nonlinear functions from a priori engineering knowledge, which are then constructed and coded into appropriate chromosome representations. Given a suitable fitness function, using evolutionary approaches such as genetic algorithms, a population of chromosomes evolves for a certain number of generations to finally produce a neural network model best fitting the system data. The ob
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Wu, Tao, Jiao Shi, Deyun Zhou, Xiaolong Zheng, and Na Li. "Evolutionary Multi-Objective One-Shot Filter Pruning for Designing Lightweight Convolutional Neural Network." Sensors 21, no. 17 (2021): 5901. http://dx.doi.org/10.3390/s21175901.

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Deep neural networks have achieved significant development and wide applications for their amazing performance. However, their complex structure, high computation and storage resource limit their applications in mobile or embedding devices such as sensor platforms. Neural network pruning is an efficient way to design a lightweight model from a well-trained complex deep neural network. In this paper, we propose an evolutionary multi-objective one-shot filter pruning method for designing a lightweight convolutional neural network. Firstly, unlike some famous iterative pruning methods, a one-shot
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Debeljak, Željko, Viktor Marohnić, Goran Srečnik, and Marica Medić-Šarić. "Novel approach to evolutionary neural network based descriptor selection and QSAR model development." Journal of Computer-Aided Molecular Design 19, no. 12 (2006): 835–55. http://dx.doi.org/10.1007/s10822-005-9022-2.

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Jung, Sung Young. "A Topographical Method for the Development of Neural Networks for Artificial Brain Evolution." Artificial Life 11, no. 3 (2005): 293–316. http://dx.doi.org/10.1162/1064546054407185.

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Developmental neural networks, which are constructed according to developmental rules (i.e., genes), have the potential to be differentiated into heteromorphic neural structures capable of performing various kinds of activities. The fact that the biological neural architectures are found to be highly repetitive, layered, and topographically organized has important consequences for neural development methods. The purpose of this article is to propose a neural development method that can construct topographical neural connections, that is, a topographical development method, to facilitate fast a
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Bury, Y. A., та D. I. Samal. "APPLICATION OF THE EVOLUTIONARY PARADIGM TO DESIGNING ARCHITEСTURE OF A NEURAL NETWORK FOR RECOGNIZING THE DISTORTED TEXT". «System analysis and applied information science», № 4 (8 лютого 2018): 45–50. http://dx.doi.org/10.21122/2309-4923-2017-4-45-50.

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The paper presents an attempt to apply of evolutionary methods to the design and training of a system for recognizing distorted text.Over the past decades, artificial neural networks are widely used in many areas of artificial intelligence, such as forecasting, optimization, data analysis, pattern recognition and decision making. Nevertheless, the traditional heuristic approaches to design of multi-layer neural networks are based on the recombination of already existing neural network architectures.This approach allows us to solve a wide range of problems, but implies compliance with specific
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Khan, Gul Muhammad, Julian F. Miller, and David M. Halliday. "Evolution of Cartesian Genetic Programs for Development of Learning Neural Architecture." Evolutionary Computation 19, no. 3 (2011): 469–523. http://dx.doi.org/10.1162/evco_a_00043.

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Although artificial neural networks have taken their inspiration from natural neurological systems, they have largely ignored the genetic basis of neural functions. Indeed, evolutionary approaches have mainly assumed that neural learning is associated with the adjustment of synaptic weights. The goal of this paper is to use evolutionary approaches to find suitable computational functions that are analogous to natural sub-components of biological neurons and demonstrate that intelligent behavior can be produced as a result of this additional biological plausibility. Our model allows neurons, de
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Dissertations / Theses on the topic "Evolutionary development of neural network"

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Bush, Brian O. "Development of a fuzzy system design strategy using evolutionary computation." Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1178656308.

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Townsend, Joseph Paul. "Artificial development of neural-symbolic networks." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/15162.

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Artificial neural networks (ANNs) and logic programs have both been suggested as means of modelling human cognition. While ANNs are adaptable and relatively noise resistant, the information they represent is distributed across various neurons and is therefore difficult to interpret. On the contrary, symbolic systems such as logic programs are interpretable but less adaptable. Human cognition is performed in a network of biological neurons and yet is capable of representing symbols, and therefore an ideal model would combine the strengths of the two approaches. This is the goal of Neural-Symbol
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Hytychová, Tereza. "Evoluční návrh neuronových sítí využívající generativní kódování." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445478.

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The aim of this work is to design and implement a method for the evolutionary design of neural networks with generative encoding. The proposed method is based on J. F. Miller's approach and uses a brain model that is gradually developed and which allows extraction of traditional neural networks. The development of the brain is controlled by programs created using cartesian genetic programming. The project was implemented in Python with the use of Numpy library. Experiments have shown that the proposed method is able to construct neural networks that achieve over 90 % accuracy on smaller datase
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Kadiyala, Akhil. "Development and Evaluation of an Integrated Approach to Study In-Bus Exposure Using Data Mining and Artificial Intelligence Methods." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1341257080.

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Adams, Bryan (Bryan Paul) 1977. "Evolutionary, developmental neural networks for robust robotic control." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/37900.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.<br>Includes bibliographical references (p. 136-143).<br>The use of artificial evolution to synthesize controllers for physical robots is still in its infancy. Most applications are on very simple robots in artificial environments, and even these examples struggle to span the "reality gap," a name given to the difference between the performance of a simulated robot and the performance of a.real robot using the same evolved controller. This dissertation describes three methods for
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Tsui, Kwok Ching. "Neural network design using evolutionary computing." Thesis, King's College London (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299918.

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Hayward, Serge. "Financial forecasting and modelling with an evolutionary artificial neural network." Thesis, Queen Mary, University of London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439394.

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Hlynka, Markian D. "A framework for an automated neural network designer using evolutionary algorithms." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0014/MQ41716.pdf.

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Jagadeesan, Ananda Prasanna. "Real time evolutionary algorithms in robotic neural control systems." Thesis, Robert Gordon University, 2006. http://hdl.handle.net/10059/436.

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This thesis describes the use of a Real-Time Evolutionary Algorithm (RTEA) to optimise an Artificial Neural Network (ANN) on-line (in this context “on-line” means while it is in use). Traditionally, Evolutionary Algorithms (Genetic Algorithms, Evolutionary Strategies and Evolutionary Programming) have been used to train networks before use - that is “off-line,” as have other learning systems like Back-Propagation and Simulated Annealing. However, this means that the network cannot react to new situations (which were not in its original training set). The system outlined here uses a Simulated L
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Jakobsson, Henrik. "Inversion of an Artificial Neural Network Mapping by Evolutionary Algorithms with Sharing." Thesis, University of Skövde, Department of Computer Science, 1998. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-165.

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<p>Inversion of the artificial neural network mapping is a relatively unexplored field of science. By inversion we mean that a search is conducted to find what input patterns that corresponds to a specific output pattern according to the analysed network. In this report, an evolutionary algorithm is proposed to conduct the search for input patterns. The hypothesis is that the inversion with the evolutionary search-method will result in multiple, separate and equivalent input patterns and not get stuck in local optima which possibly would cause the inversion to result in erroneous answer. Besid
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Books on the topic "Evolutionary development of neural network"

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Lou, Padgett Mary, Lindblad Thomas, Society for Computer Simulation, and United States. National Aeronautics and Space Administration., eds. Sixth, Seventh, and Eighth Workshops on Virtual Intelligence: Academic/Industrial/NASA/Defense: Technical interchange and tutorials : International Conferences on Virtual Intelligence, Fuzzy Systems, Evolutionary Computing, and Virtual Reality 1996. SPIE, 1996.

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C, Jain L., and Johnson R. P, eds. Automatic generation of neural network architecture using evolutionary computation. World Scientific, 1997.

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Jorgensen, Charles C. Development of a sensor coordinated kinematic model for neural network controller training. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1990.

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International, Symposium on Computational Intelligence and Design (1st 2008 Wuhan China). Proceedings of the 2008 International Symposium on Computational Intelligence and Design: October 17-18, 2008, Wuhan, China. IEEE Computer Society, 2008.

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International Symposium on Computational Intelligence and Design (2nd 2009 Changsha, China). Proceedings: 2009 International Symposium on Computational Intelligence and Design : Changsha, China, 12-14 December 2009. IEEE Computer Society, 2008.

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International Symposium on Computational Intelligence and Design (3rd 2010 Hangzhou, Zhejiang, China). Proceedings: 2010 International Symposium on Computational Intelligence and Design : ICSID 2010 : 29-31 October 2010, Hangzhou, Zhejiang, China. IEEE Computer Society, 2010.

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International Conference on Innovative Computing, Information and Control (1st 2006 Beijing, China). ICICIC 2006: First International Conference on Innovative Computing, Information and Control : 30 August-1 September, 2006, Beijing, China. Edited by Pan Jeng-Shyang, Shi Peng 1958-, Zhao Yao, and Institute of Electrical and Electronics Engineers. IEEE Computer Society, 2006.

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Topping, B. H. Developments in Neural Networks and Evolutionary Computing for Civil and Structural Engineering. Hyperion Books, 1995.

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Semi-Empirical Neural Network Modeling and Digital Twins Development. Elsevier, 2020. http://dx.doi.org/10.1016/c2017-0-02027-x.

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Software Development Outsourcing Decision Support Tool with Neural Network Learning. Storming Media, 2004.

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Book chapters on the topic "Evolutionary development of neural network"

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Cho, Sung-Bae, and Katsunori Shimohara. "Grammatical Development of Evolutionary Modular Neural Networks." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48873-1_53.

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Shakya, S., M. Kern, G. Owusu, and C. M. Chin. "Dynamic Pricing with Neural Network Demand Models and Evolutionary Algorithms." In Research and Development in Intelligent Systems XXVII. Springer London, 2010. http://dx.doi.org/10.1007/978-0-85729-130-1_16.

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Manuputty, J., P. Sen, and D. Todd. "Development of an Iterative Neural Network and Genetic Algorithm Procedure for Shipyard Scheduling." In Evolutionary Design and Manufacture. Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0519-0_27.

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Shailaja, M., and A. V. Sita Rama Raju. "Development of Back Propagation Neural Network (BPNN) Model to Predict Combustion Parameters of Diesel Engine." In Swarm, Evolutionary, and Memetic Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48959-9_7.

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Barrios, D., A. Carrascal, D. Manrique, and J. Rios. "ADANNET: Automatic Design of Artificial Neural Networks by Evolutionary Techniques." In Research and Development in Intelligent Systems XVIII. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0119-2_6.

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Dong, Xueshi, Wenyong Dong, Yunfei Yi, Yajie Wang, and Xiaosong Xu. "The Recent Developments and Comparative Analysis of Neural Network and Evolutionary Algorithms for Solving Symbolic Regression." In Intelligent Computing Theories and Methodologies. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22180-9_70.

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Rocha, Miguel, Paulo Cortez, and José Neves. "Evolutionary Neural Network Learning." In Progress in Artificial Intelligence. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24580-3_10.

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Mat Noor, R. A. "Recent Developments of Neural Networks in Biodiesel Applications." In Swarm, Evolutionary, and Memetic Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20294-5_30.

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Khan, Gul Muhammad. "Evolutionary Computation." In Evolution of Artificial Neural Development. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67466-7_3.

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Croll, Roger P. "Neural Development in Invertebrates." In The Wiley Handbook of Evolutionary Neuroscience. John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118316757.ch11.

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Conference papers on the topic "Evolutionary development of neural network"

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Roy, Anthony M., Erik K. Antonsson, and Andrew A. Shapiro. "Genetic Evolution for the Development of Robust Artificial Neural Network Logic Gates." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87448.

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Control tasks involving dramatic non-linearities, such as decision making, can be challenging for classical design methods. However, autonomous stochastic design methods have proved effective. In particular, Genetic Algorithms (GA) that create phenotypes by the application of genotypes comprising rules are robust and highly scalable. Such encodings are useful for complex applications such as artificial neural net design. This paper outlines an evolutionary algorithm that creates C++ programs which in turn create Artificial Neural Networks (ANNs) that can functionally perform as an exclusive-OR
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Miller, Julian F., and Dennis G. Wilson. "A developmental artificial neural network model for solving multiple problems." In GECCO '17: Genetic and Evolutionary Computation Conference. ACM, 2017. http://dx.doi.org/10.1145/3067695.3075976.

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Huan, Tran Thien, Cao Van Kien, and Ho Pham Huy Anh. "Adaptive Evolutionary Neural Network Gait Generation for Humanoid Robot Optimized with Modified Differential Evolution Algorithm." In 2018 4th International Conference on Green Technology and Sustainable Development (GTSD). IEEE, 2018. http://dx.doi.org/10.1109/gtsd.2018.8595586.

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LA PAZ-MARÍN, MÓNICA DE, PILAR CAMPOY-MUÑOZ, and CÉSAR HERVÁS-MARTÍNEZ. "EVOLUTIONARY NEURAL NETWORK CLASSIFIERS FOR MONITORING RESEARCH, DEVELOPMENT AND INNOVATION PERFORMANCE IN EUROPEAN UNION MEMBER STATES." In Proceedings of the XVII SIGEF Congress. WORLD SCIENTIFIC, 2012. http://dx.doi.org/10.1142/9789814415774_0021.

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Plyakin, Vladislav, and Vladislav Protasov. "Evolutionary matching method for face recognition using neural networks." In International Conference "Computing for Physics and Technology - CPT2020". ANO «Scientific and Research Center for Information in Physics and Technique», 2020. http://dx.doi.org/10.30987/conferencearticle_5fd755bf868b47.13424079.

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The problem of formalizing and automating the process of recognizing human faces was touched upon at the earliest stages of the development of image recognition systems and remains relevant to this day. Moreover, over the past ten years, the number of scientific studies and publications on this topic has increased several times, which indicates an increase in the urgency of this problem. This can be explained by the fact that modern computing technology opens up new possibilities for its application in various fields, and, accordingly, a lot of applied problems have appeared that require their
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Katragadda, Ravi Teja, Sreekanth Reddy Gondipalle, Paolo Guarneri, and Georges Fadel. "Predicting the Thermal Performance for the Multi-Objective Vehicle Underhood Packing Optimization Problem." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71098.

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The ever increasing demands towards improvement in vehicle performance and passenger comfort have led the automotive manufacturers to further enhance the design in the early stages of the vehicle development process. Though, these design changes enhance the overall vehicle performance to an extent, the placement of these components under the car hood also plays a vital role in increasing the vehicle performance. In the past, a study on the automobile underhood packaging or layout problem was conducted and a multi-objective optimization routine with three objectives namely, minimizing center of
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Weatheritt, Jack, Richard D. Sandberg, Julia Ling, Gonzalo Saez, and Julien Bodart. "A Comparative Study of Contrasting Machine Learning Frameworks Applied to RANS Modeling of Jets in Crossflow." In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-63403.

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Classical RANS turbulence models have known deficiencies when applied to jets in crossflow. Identifying the linear Boussinesq stress-strain hypothesis as a major contribution to erroneous prediction, we consider and contrast two machine learning frameworks for turbulence model development. Gene Expression Programming, an evolutionary algorithm that employs a survival of the fittest analogy, and a Deep Neural Network, based on neurological processing, add non-linear terms to the stress-strain relationship. The results are Explicit Algebraic Stress Model-like closures. High fidelity data from an
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Ivan, Zelinka, Senkerik Roman, and Oplatkova Zuzana. "Evolutionary Scanning and Neural Network Optimization." In 2008 19th International Conference on Database and Expert Systems Applications (DEXA). IEEE, 2008. http://dx.doi.org/10.1109/dexa.2008.84.

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"Evolutionary Techniques for Neural Network Optimization." In The First International Workshop on Artificial Neural Networks and Intelligent Information Processing. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0001191800030011.

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Saiki, Motohiro, and Satoshi Matsuda. "Evolutionary neural network model of universal grammar." In 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010. http://dx.doi.org/10.1109/ijcnn.2010.5596735.

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Reports on the topic "Evolutionary development of neural network"

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McDonnell, J. R., W. C. Page, and D. E. Waagen. Neural Network Construction Using Evolutionary Search. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada290862.

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Matteucci, Matteo. ELeaRNT: Evolutionary Learning of Rich Neural Network Topologies. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada456062.

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Patro, S., and W. J. Kolarik. Integrated evolutionary computation neural network quality controller for automated systems. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/350895.

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Leij, F. J., and M. T. Van Genuchten. Development of Pedotransfer Functions with Neural Network Models. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada394563.

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Fox-Rabinovitz, M. S., and V. M. Krasnopolsky. Development of Ensemble Neural Network Convection Parameterizations for Climate Models. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1039344.

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Rajagopalan, A., G. Washington, G. Rizzoni, and Y. Guezennec. Development of Fuzzy Logic and Neural Network Control and Advanced Emissions Modeling for Parallel Hybrid Vehicles. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/15006009.

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Raychev, Nikolay. Can human thoughts be encoded, decoded and manipulated to achieve symbiosis of the brain and the machine. Web of Open Science, 2020. http://dx.doi.org/10.37686/nsrl.v1i2.76.

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This article discusses the current state of neurointerface technologies, not limited to deep electrode approaches. There are new heuristic ideas for creating a fast and broadband channel from the brain to artificial intelligence. One of the ideas is not to decipher the natural codes of nerve cells, but to create conditions for the development of a new language for communication between the human brain and artificial intelligence tools. Theoretically, this is possible if the brain "feels" that by changing the activity of nerve cells that communicate with the computer, it is possible to "achieve
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