Relevant bibliographies by topics / Weight adaptation algorithms

Academic literature on the topic 'Weight adaptation algorithms'

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Published: 10 December 2022
Last updated: 19 February 2023

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Journal articles on the topic "Weight adaptation algorithms"

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Zhong, Shuiming, Yu Xue, Yunhao Jiang, Yuanfeng Jin, Jing Yang, Ping Yang, Yuan Tian, and Mznah Al-Rodhaan. "A Sensitivity-Based Improving Learning Algorithm for Madaline Rule II." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/219679.

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This paper proposes a new adaptive learning algorithm for Madalines based on a sensitivity measure that is established to investigate the effect of a Madaline weight adaptation on its output. The algorithm, following the basic idea of minimal disturbance as the MRII did, introduces an adaptation selection rule by means of the sensitivity measure to more accurately locate the weights in real need of adaptation. Experimental results on some benchmark data demonstrate that the proposed algorithm has much better learning performance than the MRII and the BP algorithms.
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Magoulas, G. D., M. N. Vrahatis, and G. S. Androulakis. "Improving the Convergence of the Backpropagation Algorithm Using Learning Rate Adaptation Methods." Neural Computation 11, no. 7 (October 1, 1999): 1769–96. http://dx.doi.org/10.1162/089976699300016223.

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This article focuses on gradient-based backpropagation algorithms that use either a common adaptive learning rate for all weights or an individual adaptive learning rate for each weight and apply the Goldstein/Armijo line search. The learning-rate adaptation is based on descent techniques and estimates of the local Lipschitz constant that are obtained without additional error function and gradient evaluations. The proposed algorithms improve the backpropagation training in terms of both convergence rate and convergence characteristics, such as stable learning and robustness to oscillations. Simulations are conducted to compare and evaluate the convergence behavior of these gradient-based training algorithms with several popular training methods.
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Yang, Feiran, Yin Cao, Ming Wu, Felix Albu, and Jun Yang. "Frequency-Domain Filtered-x LMS Algorithms for Active Noise Control: A Review and New Insights." Applied Sciences 8, no. 11 (November 20, 2018): 2313. http://dx.doi.org/10.3390/app8112313.

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This paper presents a comprehensive overview of the frequency-domain filtered-x least mean-square (FxLMS) algorithms for active noise control (ANC). The direct use of frequency-domain adaptive filters for ANC results in two kinds of delays, i.e., delay in the signal path and delay in the weight adaptation. The effects of the two kinds of delays on the convergence behavior and stability of the adaptive algorithms are analyzed in this paper. The first delay can violate the so-called causality constraint, which is a major concern for broadband ANC, and the second delay can reduce the upper bound of the step size. The modified filter-x scheme has been employed to remove the delay in the weight adaptation, and several delayless filtering approaches have been presented to remove the delay in the signal path. However, state-of-the-art frequency-domain FxLMS algorithms only remove one kind of delay, and some of these algorithms have a very high peak complexity and hence are impractical for real-time systems. This paper thus proposes a new delayless frequency-domain ANC algorithm that completely removes the two kinds of delays and has a low complexity. The performance advantages and limitations of each algorithm are discussed based on an extensive evaluation, and the complexities are evaluated in terms of both the peak and average complexities.
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Cufoglu, Ayse, Mahi Lohi, and Colin Everiss. "Feature weighted clustering for user profiling." International Journal of Modeling, Simulation, and Scientific Computing 08, no. 04 (December 2017): 1750056. http://dx.doi.org/10.1142/s1793962317500568.

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Personalization is the adaptation of the services to fit the user’s interests, characteristics and needs. The key to effective personalization is user profiling. Apart from traditional collaborative and content-based approaches, a number of classification and clustering algorithms have been used to classify user related information to create user profiles. However, they are not able to achieve accurate user profiles. In this paper, we present a new clustering algorithm, namely Multi-Dimensional Clustering (MDC), to determine user profiling. The MDC is a version of the Instance-Based Learner (IBL) algorithm that assigns weights to feature values and considers these weights for the clustering. Three feature weight methods are proposed for the MDC and, all three, have been tested and evaluated. Simulations were conducted with using two sets of user profile datasets, which are the training (includes 10,000 instances) and test (includes 1000 instances) datasets. These datasets reflect each user’s personal information, preferences and interests. Additional simulations and comparisons with existing weighted and non-weighted instance-based algorithms were carried out in order to demonstrate the performance of proposed algorithm. Experimental results using the user profile datasets demonstrate that the proposed algorithm has better clustering accuracy performance compared to other algorithms. This work is based on the doctoral thesis of the corresponding author.
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Wu, Di, Sheng Yao Yang, and J. C. Liu. "Cognitive Radio Decision Engine Based on Multi-Objective Genetic Algorithm." Applied Mechanics and Materials 48-49 (February 2011): 314–17. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.314.

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The performance optimization of cognitive radio is a multi-objective optimization problem. Existing genetic algorithms are difficult to assign the weight of each objective when the linear weighting method is used to simplify the multi-objective optimization problem into a single objective optimization problem. In this paper, we propose a new cognitive decision engine algorithm using multi-objective genetic algorithm with population adaptation. A multicarrier system is used for simulation analysis, and experimental results show that the proposed algorithm is effective and meets the real-time requirement.
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Yiou, Pascal, and Aglaé Jézéquel. "Simulation of extreme heat waves with empirical importance sampling." Geoscientific Model Development 13, no. 2 (February 25, 2020): 763–81. http://dx.doi.org/10.5194/gmd-13-763-2020.

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Abstract. Simulating ensembles of extreme events is a necessary task to evaluate their probability distribution and analyze their meteorological properties. Algorithms of importance sampling have provided a way to simulate trajectories of dynamical systems (like climate models) that yield extreme behavior, like heat waves. Such algorithms also give access to the return periods of such events. We present an adaptation based on circulation analogues of importance sampling to provide a data-based algorithm that simulates extreme events like heat waves in a realistic way. This algorithm is a modification of a stochastic weather generator, which gives more weight to trajectories with higher temperatures. This presentation outlines the methodology using European heat waves and illustrates the spatial and temporal properties of simulations.
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Khabarlak, K. S. "FASTER OPTIMIZATION-BASED META-LEARNING ADAPTATION PHASE." Radio Electronics, Computer Science, Control, no. 1 (April 7, 2022): 82. http://dx.doi.org/10.15588/1607-3274-2022-1-10.

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Context. Neural networks require a large amount of annotated data to learn. Meta-learning algorithms propose a way to decrease number of training samples to only a few. One of the most prominent optimization-based meta-learning algorithms is MAML. However, its adaptation to new tasks is quite slow. The object of study is the process of meta-learning and adaptation phase as defined by the MAML algorithm.Objective. The goal of this work is creation of an approach, which should make it possible to: 1) increase the execution speed of MAML adaptation phase; 2) improve MAML accuracy in certain cases. The testing results will be shown on a publicly available few-shot learning dataset CIFAR-FS.Method. In this work an improvement to MAML meta-learning algorithm is proposed. Meta-learning procedure is defined in terms of tasks. In case of image classification problem, each task is to try to learn to classify images of new classes given only a few training examples. MAML defines 2 stages for the learning procedure: 1) adaptation to the new task; 2) meta-weights update. The whole training procedure requires Hessian computation, which makes the method computationally expensive. After being trained, the network will typically be used for adaptation to new tasks and the subsequent prediction on them. Thus, improving adaptation time is an important problem, which we focus on in this work. We introduce lambda pattern by which we restrict which weight we update in the network during the adaptation phase. This approach allows us to skip certain gradient computations. The pattern is selected given an allowed quality degradation threshold parameter. Among the pattern that fit the criteria, the fastest pattern is then selected. However, as it is discussed later, quality improvement is also possible is certain cases by a careful pattern selection.Results. The MAML algorithm with lambda pattern adaptation has been implemented, trained and tested on the open CIFAR-FS dataset. This makes our results easily reproducible.Conclusions. The experiments conducted have shown that via lambda adaptation pattern selection, it is possible to significantly improve the MAML method in the following areas: adaptation time has been decreased by a factor of 3 with minimal accuracy loss. Interestingly, accuracy for one-step adaptation has been substantially improved by using lambda patterns as well. Prospects for further research are to investigate a way of a more robust automatic pattern selection scheme.
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Li, Miqing, and Xin Yao. "What Weights Work for You? Adapting Weights for Any Pareto Front Shape in Decomposition-Based Evolutionary Multiobjective Optimisation." Evolutionary Computation 28, no. 2 (June 2020): 227–53. http://dx.doi.org/10.1162/evco_a_00269.

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The quality of solution sets generated by decomposition-based evolutionary multi-objective optimisation (EMO) algorithms depends heavily on the consistency between a given problem's Pareto front shape and the specified weights' distribution. A set of weights distributed uniformly in a simplex often leads to a set of well-distributed solutions on a Pareto front with a simplex-like shape, but may fail on other Pareto front shapes. It is an open problem on how to specify a set of appropriate weights without the information of the problem's Pareto front beforehand. In this article, we propose an approach to adapt weights during the evolutionary process (called AdaW). AdaW progressively seeks a suitable distribution of weights for the given problem by elaborating several key parts in weight adaptation—weight generation, weight addition, weight deletion, and weight update frequency. Experimental results have shown the effectiveness of the proposed approach. AdaW works well for Pareto fronts with very different shapes: 1) the simplex-like, 2) the inverted simplex-like, 3) the highly nonlinear, 4) the disconnect, 5) the degenerate, 6) the scaled, and 7) the high-dimensional.
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Miertoiu, Florin Ilarion, and Bogdan Dumitrescu. "Feasibility Pump Algorithm for Sparse Representation under Gaussian Noise." Algorithms 13, no. 4 (April 9, 2020): 88. http://dx.doi.org/10.3390/a13040088.

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In this paper, the Feasibility Pump is adapted for the problem of sparse representations of signals affected by Gaussian noise. This adaptation is tested and then compared to Orthogonal Matching Pursuit (OMP) and the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). The feasibility pump recovers the true support much better than the other two algorithms and, as the SNR decreases and the support size increases, it has a smaller recovery and representation error when compared with its competitors. It is observed that, in order for the algorithm to be efficient, a regularization parameter and a weight term for the error are needed.
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Szuster, Marcin, and Piotr Gierlak. "Globalized Dual Heuristic Dynamic Programming in Control of Robotic Manipulator." Applied Mechanics and Materials 817 (January 2016): 150–61. http://dx.doi.org/10.4028/www.scientific.net/amm.817.150.

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The article focuses on the implementation of the globalized dual-heuristic dynamic programming algorithm in the discrete tracking control system of the three degrees of freedom robotic manipulator. The globalized dual-heuristic dynamic programming algorithm is included in the approximate dynamic programming algorithms family, that bases on the Bellman’s dynamic programming idea. These algorithms generally consist of the actor and the critic structures realized in a form of artificial neural networks. Moreover, the control system includes the PD controller, the supervisory term and an additional control signal. The structure of the supervisory term derives from the stability analysis, which was realized using the Lyapunov stability theorem. The control system works on-line and the neural networks’ weight adaptation process is realized in every iteration step. A series of computer simulations was realized in Matlab/Simulink software to confirm performance of the control system.
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Dissertations / Theses on the topic "Weight adaptation algorithms"

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Haberstich, Cécile. "Adaptive approximation of high-dimensional functions with tree tensor networks for Uncertainty Quantification." Thesis, Ecole centrale de Nantes, 2020. http://www.theses.fr/2020ECDN0045.

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Les problèmes de quantification d'incertitudes des modèles numériques nécessitent de nombreuses simulations, souvent très coûteuses (en temps de calcul et/ou en mémoire). C'est pourquoi il est essentiel de construire des modèles approchés qui sont moins coûteux à évaluer. En pratique, si la réponse d'un modèle numérique est représentée par une fonction, on cherche à en construire une approximation.L'objectif de cette thèse est de construire l'approximation d'une fonction qui soit contrôlée tout en utilisant le moins d'évaluations possible de la fonction.Dans un premier temps, nous proposons une nouvelle méthode basée sur les moindres carrés pondérés pour construire l'approximation d'une fonction dans un espace vectoriel. Nous prouvons que la projection vérifie une propriété de stabilité numérique presque sûrement et une propriété de quasi-optimalité en espérance. En pratique on observe que la taille de l'échantillon est plus proche de la dimension de l'espace d'approximation que pour les autres techniques de moindres carrés pondérées existantes.Pour l'approximation en grande dimension et afin d’exploiter de potentielles structures de faible dimension, nous considérons dans cette thèse des approximations dans des formats de tenseurs basés sur des arbres. Ces formats admettent une paramétrisation multilinéaire avec des paramètres formant un réseau de tenseurs de faible ordre et sont ainsi également appelés réseaux de tenseurs basés sur des arbres. Dans cette thèse, nous proposons un algorithme pour construire l'approximation de fonctions dans des formats de tenseurs basés sur des arbres. Il consiste à construire une hiérarchie de sous-espaces imbriqués associés aux différents niveaux de l'arbre. La construction de ces espaces s'appuie sur l'analyse en composantes principales étendue aux fonctions multivariées et sur l'utilisation de la nouvelle méthode des moindres carrés pondérés. Afin de réduire le nombre d'évaluations nécessaires pour construire l'approximation avec une certaine précision, nous proposons des stratégies adaptatives pour le contrôle de l'erreur de discrétisation, la sélection de l'arbre, le contrôle des rangs et l'estimation des composantes principales
Uncertainty quantification problems for numerical models require a lot of simulations, often very computationally costly (in time and/or memory). This is why it is essential to build surrogate models that are cheaper to evaluate. In practice, the output of a numerical model is represented by a function, then the objective is to construct an approximation.The aim of this thesis is to construct a controlled approximation of a function while using as few evaluations as possible.In a first time, we propose a new method based on weighted least-squares to construct the approximation of a function onto a linear approximation space. We prove that the projection verifies a numerical stability property almost surely and a quasi-optimality property in expectation. In practice we observe that the sample size is closer to the dimension of the approximation space than with existing weighted least-squares methods.For high-dimensional approximation, and in order to exploit potential low-rank structures of functions, we consider the model class of functions in tree-based tensor formats. These formats admit a multilinear parametrization with parameters forming a tree network of low-order tensors and are therefore also called tree tensor networks. In this thesis we propose an algorithm for approximating functions in tree-based tensor formats. It consists in constructing a hierarchy of nested subspaces associated to the different levels of the tree. The construction of these subspaces relies on principal component analysis extended to multivariate functions and the new weighted least-squares method. To reduce the number of evaluations necessary to build the approximation with a certain precision, we propose adaptive strategies for the control of the discretization error, the tree selection, the control of the ranks and the estimation of the principal components
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(9828605), S. M. Rahman. "A feedforward neural network and its application to system indentification and control." Thesis, 1996. https://figshare.com/articles/thesis/A_feedforward_neural_network_and_its_application_to_system_indentification_and_control/20346819.

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 The aim of this thesis is to study a feedforward neural network and its application to system identification and control. 

Attention is focused firstly on feedforward neural networks and their weight adaptation algorithms. A new class of weight adaptation learning algorithms are introduced based on the sliding mode concept. The effectiveness of the new class of algorithms are studied and simulations are conducted to present their performance. 

Second part of this thesis deals with the application of the feedforward neural network with the developed learning algorithms. Two classes of problems are chosen to test the suitability of the feedforward neural network with proposed adaptation learning algorithms. The first problem is dynamic system identification and the other is dynamic system control. Results are presented in this thesis show the effectiveness of the feedforward neural network with the proposed learning algorithms in system identification and control.  

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Books on the topic "Weight adaptation algorithms"

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Chistyakova, Guzel, Lyudmila Ustyantseva, Irina Remizova, Vladislav Ryumin, and Svetlana Bychkova. CHILDREN WITH EXTREMELY LOW BODY WEIGHT: CLINICAL CHARACTERISTICS, FUNCTIONAL STATE OF THE IMMUNE SYSTEM, PATHOGENETIC MECHANISMS OF THE FORMATION OF NEONATAL PATHOLOGY. au: AUS PUBLISHERS, 2022. http://dx.doi.org/10.26526/monography_62061e70cc4ed1.46611016.

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The purpose of the monograph, which contains a modern view of the problem of adaptation of children with extremely low body weight, is to provide a wide range of doctors with basic information about the clinical picture, functional activity of innate and adaptive immunity, prognostic criteria of postnatal pathology, based on their own research. The specific features of the immunological reactivity of premature infants of various gestational ages who have developed bronchopulmonary dysplasia (BPD) and retinopathy of newborns (RN) from the moment of birth and after reaching postconceptional age (37-40 weeks) are described separately. The mechanisms of their implementation with the participation of factors of innate and adaptive immunity are considered in detail. Methods for early prediction of BPD and RN with the determination of an integral indicator and an algorithm for the management of premature infants with a high risk of postnatal complications at the stage of early rehabilitation are proposed. The information provided makes it possible to personify the treatment, preventive and rehabilitation measures in premature babies. The monograph is intended for obstetricians-gynecologists, neonatologists, pediatricians, allergists-immunologists, doctors of other specialties, residents, students of the system of continuing medical education. This work was done with financial support from the Ministry of Education and Science, grant of the President of the Russian Federation No. MK-1140.2020.7.
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Book chapters on the topic "Weight adaptation algorithms"

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Chistyakova, Guzel, Lyudmila Ustyantseva, Irina Remizova, Vladislav Ryumin, and Svetlana Bychkova. "CHARACTERISTICS OF CONNECTED AND ADAPTIVE IMMUNITY OF CHILDREN WITH EXTREMELY LOW BODY WEIGHT OF DIFFERENT GESTIONAL AGE." In CHILDREN WITH EXTREMELY LOW BODY WEIGHT: CLINICAL CHARACTERISTICS, FUNCTIONAL STATE OF THE IMMUNE SYSTEM, PATHOGENETIC MECHANISMS OF THE FORMATION OF NEONATAL PATHOLOGY, 47–77. au: AUS PUBLISHERS, 2022. http://dx.doi.org/10.26526/chapter_62061e70deca75.92242970.

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The purpose of the monograph, which contains a modern view of the problem of adaptation of children with extremely low body weight, is to provide a wide range of doctors with basic information about the clinical picture, functional activity of innate and adaptive immunity, prognostic criteria of postnatal pathology, based on their own research. The specific features of the immunological reactivity of premature infants of various gestational ages who have developed bronchopulmonary dysplasia (BPD) and retinopathy of newborns (RN) from the moment of birth and after reaching postconceptional age (37-40 weeks) are described separately. The mechanisms of their implementation with the participation of factors of innate and adaptive immunity are considered in detail. Methods for early prediction of BPD and RN with the determination of an integral indicator and an algorithm for the management of premature infants with a high risk of postnatal complications at the stage of early rehabilitation are proposed. The information provided makes it possible to personify the treatment, preventive and rehabilitation measures in premature babies. The monograph is intended for obstetricians-gynecologists, neonatologists, pediatricians, allergists-immunologists, doctors of other specialties, residents, students of the system of continuing medical education. This work was done with financial support from the Ministry of Education and Science, grant of the President of the Russian Federation No. MK-1140.2020.7.
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Chistyakova, Guzel, Lyudmila Ustyantseva, Irina Remizova, Vladislav Ryumin, and Svetlana Bychkova. "FUNCTIONAL STATE OF THE IMMUNE SYSTEM OF CHILDREN WITH RETINOPATHY OF PREMATURE IN THE DYNAMICS OF THE POSTNATAL PERIOD." In CHILDREN WITH EXTREMELY LOW BODY WEIGHT: CLINICAL CHARACTERISTICS, FUNCTIONAL STATE OF THE IMMUNE SYSTEM, PATHOGENETIC MECHANISMS OF THE FORMATION OF NEONATAL PATHOLOGY, 105–28. au: AUS PUBLISHERS, 2022. http://dx.doi.org/10.26526/chapter_62061e70e0ba78.92986346.

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The purpose of the monograph, which contains a modern view of the problem of adaptation of children with extremely low body weight, is to provide a wide range of doctors with basic information about the clinical picture, functional activity of innate and adaptive immunity, prognostic criteria of postnatal pathology, based on their own research. The specific features of the immunological reactivity of premature infants of various gestational ages who have developed bronchopulmonary dysplasia (BPD) and retinopathy of newborns (RN) from the moment of birth and after reaching postconceptional age (37-40 weeks) are described separately. The mechanisms of their implementation with the participation of factors of innate and adaptive immunity are considered in detail. Methods for early prediction of BPD and RN with the determination of an integral indicator and an algorithm for the management of premature infants with a high risk of postnatal complications at the stage of early rehabilitation are proposed. The information provided makes it possible to personify the treatment, preventive and rehabilitation measures in premature babies. The monograph is intended for obstetricians-gynecologists, neonatologists, pediatricians, allergists-immunologists, doctors of other specialties, residents, students of the system of continuing medical education. This work was done with financial support from the Ministry of Education and Science, grant of the President of the Russian Federation No. MK-1140.2020.7.
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Chistyakova, Guzel, Lyudmila Ustyantseva, Irina Remizova, Vladislav Ryumin, and Svetlana Bychkova. "FEATURES OF THE FUNCTIONAL STATE OF THE IMMUNE SYSTEM OF NEWBORNS WITH BRONCHOPULMONARY DYSPLASIA." In CHILDREN WITH EXTREMELY LOW BODY WEIGHT: CLINICAL CHARACTERISTICS, FUNCTIONAL STATE OF THE IMMUNE SYSTEM, PATHOGENETIC MECHANISMS OF THE FORMATION OF NEONATAL PATHOLOGY, 78–104. au: AUS PUBLISHERS, 2022. http://dx.doi.org/10.26526/chapter_62061e70dfbae2.28992721.

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The purpose of the monograph, which contains a modern view of the problem of adaptation of children with extremely low body weight, is to provide a wide range of doctors with basic information about the clinical picture, functional activity of innate and adaptive immunity, prognostic criteria of postnatal pathology, based on their own research. The specific features of the immunological reactivity of premature infants of various gestational ages who have developed bronchopulmonary dysplasia (BPD) and retinopathy of newborns (RN) from the moment of birth and after reaching postconceptional age (37-40 weeks) are described separately. The mechanisms of their implementation with the participation of factors of innate and adaptive immunity are considered in detail. Methods for early prediction of BPD and RN with the determination of an integral indicator and an algorithm for the management of premature infants with a high risk of postnatal complications at the stage of early rehabilitation are proposed. The information provided makes it possible to personify the treatment, preventive and rehabilitation measures in premature babies. The monograph is intended for obstetricians-gynecologists, neonatologists, pediatricians, allergists-immunologists, doctors of other specialties, residents, students of the system of continuing medical education. This work was done with financial support from the Ministry of Education and Science, grant of the President of the Russian Federation No. MK-1140.2020.7.
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Chistyakova, Guzel, Lyudmila Ustyantseva, Irina Remizova, Vladislav Ryumin, and Svetlana Bychkova. "FEATURES OF THE POSTNATAL PERIOD OF PREMATURE INFANTS." In CHILDREN WITH EXTREMELY LOW BODY WEIGHT: CLINICAL CHARACTERISTICS, FUNCTIONAL STATE OF THE IMMUNE SYSTEM, PATHOGENETIC MECHANISMS OF THE FORMATION OF NEONATAL PATHOLOGY, 25–46. au: AUS PUBLISHERS, 2022. http://dx.doi.org/10.26526/chapter_62061e70ddd515.23232017.

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The purpose of the monograph, which contains a modern view of the problem of adaptation of children with extremely low body weight, is to provide a wide range of doctors with basic information about the clinical picture, functional activity of innate and adaptive immunity, prognostic criteria of postnatal pathology, based on their own research. The specific features of the immunological reactivity of premature infants of various gestational ages who have developed bronchopulmonary dysplasia (BPD) and retinopathy of newborns (RN) from the moment of birth and after reaching postconceptional age (37-40 weeks) are described separately. The mechanisms of their implementation with the participation of factors of innate and adaptive immunity are considered in detail. Methods for early prediction of BPD and RN with the determination of an integral indicator and an algorithm for the management of premature infants with a high risk of postnatal complications at the stage of early rehabilitation are proposed. The information provided makes it possible to personify the treatment, preventive and rehabilitation measures in premature babies. The monograph is intended for obstetricians-gynecologists, neonatologists, pediatricians, allergists-immunologists, doctors of other specialties, residents, students of the system of continuing medical education. This work was done with financial support from the Ministry of Education and Science, grant of the President of the Russian Federation No. MK-1140.2020.7.
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Chistyakova, Guzel, Lyudmila Ustyantseva, Irina Remizova, Vladislav Ryumin, and Svetlana Bychkova. "RISK FACTORS OF BIRTH OF PREMATURE CHILDREN." In CHILDREN WITH EXTREMELY LOW BODY WEIGHT: CLINICAL CHARACTERISTICS, FUNCTIONAL STATE OF THE IMMUNE SYSTEM, PATHOGENETIC MECHANISMS OF THE FORMATION OF NEONATAL PATHOLOGY, 11–24. au: AUS PUBLISHERS, 2022. http://dx.doi.org/10.26526/chapter_62061e70dcd948.10387409.

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The purpose of the monograph, which contains a modern view of the problem of adaptation of children with extremely low body weight, is to provide a wide range of doctors with basic information about the clinical picture, functional activity of innate and adaptive immunity, prognostic criteria of postnatal pathology, based on their own research. The specific features of the immunological reactivity of premature infants of various gestational ages who have developed bronchopulmonary dysplasia (BPD) and retinopathy of newborns (RN) from the moment of birth and after reaching postconceptional age (37-40 weeks) are described separately. The mechanisms of their implementation with the participation of factors of innate and adaptive immunity are considered in detail. Methods for early prediction of BPD and RN with the determination of an integral indicator and an algorithm for the management of premature infants with a high risk of postnatal complications at the stage of early rehabilitation are proposed. The information provided makes it possible to personify the treatment, preventive and rehabilitation measures in premature babies. The monograph is intended for obstetricians-gynecologists, neonatologists, pediatricians, allergists-immunologists, doctors of other specialties, residents, students of the system of continuing medical education. This work was done with financial support from the Ministry of Education and Science, grant of the President of the Russian Federation No. MK-1140.2020.7.
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Serra, Ginalber Luiz de Oliveira, and Edson B. M. Costa. "Robust Stability Self-Tuning Fuzzy PID Digital Controller." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 141–54. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3129-6.ch006.

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A self-tuning fuzzy control methodology via particle swarm optimization based on robust stability criterion, is proposed. The plant to be controlled is modeled considering a Takagi-Sugeno (TS) fuzzy structure from input-output experimental data, by using the fuzzy C-Means clustering algorithm (antecedent parameters estimation) and weighted recursive least squares (WRLS) algorithm (consequent parameters estimation), respectively. An adaptation mechanism based on particle swarm optimization is used to tune recursively the parameters of a fuzzy PID controller, from the gain and phase margins specifications. Computational results for adaptive fuzzy control of a thermal plant with time varying delay is presented to illustrate the efficiency and applicability of the proposed methodology.
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Abdelbar, Ashraf M., Islam Elnabarawy, Donald C. Wunsch II, and Khalid M. Salama. "Ant Colony Optimization Applied to the Training of a High Order Neural Network with Adaptable Exponential Weights." In Advances in Computational Intelligence and Robotics, 362–74. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0063-6.ch014.

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High order neural networks (HONN) are neural networks which employ neurons that combine their inputs non-linearly. The HONEST (High Order Network with Exponential SynapTic links) network is a HONN that uses neurons with product units and adaptable exponents. The output of a trained HONEST network can be expressed in terms of the network inputs by a polynomial-like equation. This makes the structure of the network more transparent and easier to interpret. This study adapts ACOR, an Ant Colony Optimization algorithm, to the training of an HONEST network. Using a collection of 10 widely-used benchmark datasets, we compare ACOR to the well-known gradient-based Resilient Propagation (R-Prop) algorithm, in the training of HONEST networks. We find that our adaptation of ACOR has better test set generalization than R-Prop, though not to a statistically significant extent.
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Abdelbar, Ashraf M., Islam Elnabarawy, Donald C. Wunsch II, and Khalid M. Salama. "Ant Colony Optimization Applied to the Training of a High Order Neural Network with Adaptable Exponential Weights." In Deep Learning and Neural Networks, 82–95. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch006.

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High order neural networks (HONN) are neural networks which employ neurons that combine their inputs non-linearly. The HONEST (High Order Network with Exponential SynapTic links) network is a HONN that uses neurons with product units and adaptable exponents. The output of a trained HONEST network can be expressed in terms of the network inputs by a polynomial-like equation. This makes the structure of the network more transparent and easier to interpret. This study adapts ACOℝ, an Ant Colony Optimization algorithm, to the training of an HONEST network. Using a collection of 10 widely-used benchmark datasets, we compare ACOℝ to the well-known gradient-based Resilient Propagation (R-Prop) algorithm, in the training of HONEST networks. We find that our adaptation of ACOℝ has better test set generalization than R-Prop, though not to a statistically significant extent.
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Goldberg, David E. "John H. Holland, Facetwise Models, and Economy of Thought." In Perspectives on Adaptation in Natural and Artificial Systems. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780195162929.003.0008.

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Noted complex adaptive system researcher John H. Holland now receives acclaim from many quarters, but it is important to understand that this man and his ideas have been controversial since the beginning of his career. Genetic algorithms (GAs) were ignored or disparaged throughout the 1960s and 1970s, and even now, as these and his other ideas receive worldwide recognition in broad outline, the specifics of his mode of thought and insight are rejected by many who claim to embrace his key insights. This is a mistake. I have known John Holland for 23 years, and I have learned many things from him, but a critical influence has been his style of thought, in particular, his style of modeling. John has an uncanny knack of getting to the heart of a matter through the construction of what I call little models. Sometimes these models are verbal, sometimes they are mathematical, but they almost always shed a great deal of light on some nagging question in the analysis and design of complex systems. In this chapter, I propose to briefly explore John Holland's style of little modeling, and better understand its nature, its essence, and why some of those who embrace the broad outlines of his teaching have been slow to embrace the details of his modeling and the style of his thought. The exploration begins by recalling my own first impressions of John Holland and his style of thought, impressions made 23 years ago in a classroom in Ann Arbor, Michigan. It continues with a case study in Holland-style facetwise model building in constructing a takeover time model. It continues by integrating the takeover time model with a model of innovation on dimensional grounds. Finally, the Hollandian mode of model building is placed on intellectual terra firma with an economic argument, suggesting that the costs of modeling or thought must be weighed in relation to the model's benefits in understanding or designing a complex system.
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Conference papers on the topic "Weight adaptation algorithms"

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Zein-Sabatto, Saleh, Alireza Behbahani, Richard Hans Mgaya, and Mohammad Bodruzzaman. "Turbine Engine Reconfigurable Control Systems for Aircraft Propulsion Performance Improvement." In ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-94910.

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Artificial intelligent technologies are being investigated for development of advanced adaptation and reconfiguration algorithms for turbine engine distributed control systems. Adding adaptation and reconfiguration feature to the control systems is expected to improve engine performance and provide more efficient aircraft operations. The benefits of transitioning from the existing centralized supervisory control, the Full Authority Digital Electronic Control (FADEC), of turbine engines to distributed control architecture have been well articulated in the literature. The major benefits are summarized as; weight reduction, cost saving, and damage tolerant turbine engines. However, consideration of advanced intelligent technologies in the design and operation of distributed control systems has been a challenging task and requires further investigation. This paper describes research activities to address the above stated challenge leading to fully reconfigurable distributed control systems for turbine engines of the future aircrafts. The goal of the research work is to accelerate the development and implementation of disturbed control systems in aircrafts propulsion controls and its integration with distributed diagnostics and prognostics algorithms. This will be achieved by means of using artificial intelligent technologies to design adaptation and reconfiguration algorithms and integrate it with a set of engine decentralized controllers. Fuzzy inference concept will be used to develop and implement the proposed adaptation and reconfiguration algorithms. Finally, the integrated reconfiguration algorithms with the control system will be tested and verified on publicly available turbine engine simulation software.
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Sanjuan, Marco E. "Neural-Network Based On-Line Adaptation of Model Predictive Controller for Dynamic Systems With Uncertain Behavior." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-82609.

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Model-based controllers have positioned themselves in industrial applications, working mainly on top of a layer of PID controllers. Their implementation takes an important amount of time because of the required PID tuning and the model characterization/identification. This paper presents a strategy to perform on-line adaptation for the dynamic matrix coefficients in a DMC controller. Based on the observed PH (Prediction Horizon) elements of the response and controller signal vectors, and based on a non-residing control horizon controller design, the direct control problem is reformulated using the full-effect dynamic matrix (PHxPH) as an unknown. Data is collected and used in two directions: training a RAWN Network (Random Allocation Weight Neural Network), to describe recently observed process behavior, and to solve a least-squares problem for a set of linear equations where the unknowns are the characteristic response coefficients. The paper presents the effect of both approaches, illustrating the adaptation algorithms operation in a highly nonlinear process where the controller is designed in a low-gain region. Then the process operating condition is shifted so that it moves to a high-gain region to observe controller response.
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Wang, Cheng, Chang-qi Yan, Jian-jun Wang, Lei Chen, and Gui-jing Li. "Application of Dual-Adaptive Niched Genetic Algorithm in Optimal Design of Nuclear Power Components." In 2014 22nd International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/icone22-30120.

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Genetic algorithm (GA) has been widely applied in optimal design of nuclear power components. Simple genetic algorithm (SGA) has the defects of poor convergence accuracy and easily falling into the local optimum when dealing with nonlinear constraint optimization problem. To overcome these defects, an improved genetic algorithm named dual-adaptive niched genetic algorithm (DANGA) is designed in this work. The new algorithm adopts niche technique to enhance global search ability, which utilizes a sharing function to maintain population diversity. Dual-adaptation technique is developed to improve the global and local search capability at the same time. Furthermore, a new reconstitution operator is applied to the DANGA to handle the constraint conditions, which can avoid the difficulty of selecting punishment parameter when using the penalty function method. The performance of new algorithm is evaluated by optimizing the benchmark function. The volume optimization of the Qinshan I steam generator and the weight optimization of Qinshan I condenser, taking thermal-hydraulic and geometric constraints into consideration, is carried out by adopting the DANGA. The result of benchmark function test shows that the new algorithm is more effective than some traditional genetic algorithms. The optimization design shows obvious validity and can provide guidance for real engineering design.
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Dominique, S., and J. Y. Tre´panier. "Automated Preliminary Structural Rotor Design Using Genetic Algorithms and Neural Networks." In ASME Turbo Expo 2008: Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-51181.

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It is important for any design process to have a good starting point in order to reduce the cycle time and the number of design iterations required. This paper presents an automated preliminary structural design system for a gas turbine rotor, using only preliminary aerodynamic data and a simplified structural analysis, with the objective of producing a good, feasible starting solution for the blades and the disc. The process starts with a CBR (case-based reasoning) algorithm coupled with a databank of existing solutions. The algorithm uses a neural network to choose from among the closest existing rotor solutions and interpolate between them. These designs, along with the interpolated solution, will constitute the initial set of possible designs. An adaptation algorithm then processes each possible design using simplified analysis to compute the estimated sensitivity of the design function with respect to each parameter in the neighbourhood of these design solutions. The algorithm uses those sensitivities to separate the design parameters into several layers according to their relative importance. In a following phase, these design solutions are used to train a surrogate neural network model for the function, and also as the starting population for a genetic algorithm (GA). The GA is then run, with the objective of minimizing the weight of the rotor while respecting stress and aerodynamics constraints. More parameter sets (beginning with the most important) are gradually added as an input for each GA run. Although this process would not be capable of replacing a detailed design system, as it currently uses only simplified analysis, it can provide a concept designer with a very good starting solution within a relatively short computing time.
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Zuo, Lei, and Samir A. Nayfeh. "Adaptive Least-Mean Square Feed-Forward Control With Actuator Saturation by Direct Minimization." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85494.

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The least-mean squares (LMS) adaptive feedforward algorithm is used widely for vibration and noise cancellation. If reference signals become large enough to saturate that actuators, the filter coefficients in such algorithms can diverge. The leaky LMS method limits the controller effort by augmenting the objective function by a weighted control effort, and is known to attain good performance and avoid growth of filter coefficients for well-chosen weights. We propose an algorithm that seeks to directly minimize the mean-square cost in the presence of saturation. We derive the true stochastic gradient of the cost for systems with saturation with respect to the filter coefficients and obtain an adaptation rule very close to that of the filtered-x algorithm, but in the proposed algorithm, the reference filter is a time-varying modification of the secondary channel. In simulations of an active vibration isolation system with actuator limits subject to random ground vibration, the leaky LMS algorithm attains its best performance with actuation weights small enough to allow significant actuator saturation but large enough to prevent divergence. The proposed algorithm attains performance better that attained by the leaky LMS algorithm, and does not require the selection of weights.
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Steffens Henrique, Alisson, Vinicius Almeida dos Santos, and Rodrigo Lyra. "NEAT Snake: a both evolutionary and neural network adaptation approach." In Computer on the Beach. Itajaí: Universidade do Vale do Itajaí, 2020. http://dx.doi.org/10.14210/cotb.v11n1.p052-053.

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There are several challenges when modeling artificial intelligencemethods for autonomous players on games (bots). NEAT is one ofthe models that, combining genetic algorithms and neural networks,seek to describe a bot behavior more intelligently. In NEAT, a neuralnetwork is used for decision making, taking relevant inputs fromthe environment and giving real-time decisions. In a more abstractway, a genetic algorithm is applied for the learning step of the neuralnetworks’ weights, layers, and parameters. This paper proposes theuse of relative position as the input of the neural network, basedon the hypothesis that the bot profit will be improved.
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Bouvier, Victor, Philippe Very, Clément Chastagnol, Myriam Tami, and Céline Hudelot. "Robust Domain Adaptation: Representations, Weights and Inductive Bias (Extended Abstract)." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/644.

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Domain Invariant Representations (IR) has improved drastically the transferability of representations from a labelled source domain to a new and unlabelled target domain. Unsupervised Domain Adaptation (UDA) in presence of label shift remains an open problem. To this purpose, we present a bound of the target risk which incorporates both weights and invariant representations. Our theoretical analysis highlights the role of inductive bias in aligning distributions across domains. We illustrate it on standard benchmarks by proposing a new learning procedure for UDA. We observed empirically that weak inductive bias makes adaptation robust to label shift. The elaboration of stronger inductive bias is a promising direction for new UDA algorithms.
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Li, Zhixiang, Liang He, and Yanjie Chu. "An Improved Decomposition Multiobjective Optimization Algorithm with Weight Vector Adaptation Strategy." In 2017 13th International Conference on Semantics, Knowledge and Grids (SKG). IEEE, 2017. http://dx.doi.org/10.1109/skg.2017.00012.

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Hu, Shiqiang, and Zhongliang Jing. "A New Fusion Tracking Algorithm Based on Extended Adalines Model." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-42468.

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An approach of multi-sensor fusion tracking with extended Adalines model and fuzzy stochastic decision is proposed in this paper. The criterion of multi-sensor fuzzy stochastic decision is presented. An optimal algorithm for weight adjustment is designed combining genetic algorithm with fuzzy reasoning. This optimal algorithm has two important features: adaptation and learning; the effectiveness of the proposed method is illustrated through computer simulation under the circumstances of multi-sensor in target tracking.
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Junqueira, Paulo Pinheiro, Ivan Reinaldo Meneghini, and Frederico Gadelha Guimaraes. "Local Neighborhood-Based Adaptation of Weights in Multi-Objective Evolutionary Algorithms Based on Decomposition." In 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2021. http://dx.doi.org/10.1109/cec45853.2021.9504688.

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