Bibliografías temáticas / Neural network adaptation

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Literatura académica sobre el tema "Neural network adaptation"

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Publicado: 25 de mayo de 2024

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Artículos de revistas sobre el tema "Neural network adaptation"

Hylton, Todd. "Thermodynamic Neural Network". Entropy 22, n.º 3 (25 de febrero de 2020): 256. http://dx.doi.org/10.3390/e22030256.

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A thermodynamically motivated neural network model is described that self-organizes to transport charge associated with internal and external potentials while in contact with a thermal reservoir. The model integrates techniques for rapid, large-scale, reversible, conservative equilibration of node states and slow, small-scale, irreversible, dissipative adaptation of the edge states as a means to create multiscale order. All interactions in the network are local and the network structures can be generic and recurrent. Isolated networks show multiscale dynamics, and externally driven networks evolve to efficiently connect external positive and negative potentials. The model integrates concepts of conservation, potentiation, fluctuation, dissipation, adaptation, equilibration and causation to illustrate the thermodynamic evolution of organization in open systems. A key conclusion of the work is that the transport and dissipation of conserved physical quantities drives the self-organization of open thermodynamic systems.

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Vreeswijk, C. van y D. Hansel. "Patterns of Synchrony in Neural Networks with Spike Adaptation". Neural Computation 13, n.º 5 (1 de mayo de 2001): 959–92. http://dx.doi.org/10.1162/08997660151134280.

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We study the emergence of synchronized burst activity in networks of neurons with spike adaptation. We show that networks of tonically firing adapting excitatory neurons can evolve to a state where the neurons burst in a synchronized manner. The mechanism leading to this burst activity is analyzed in a network of integrate-and-fire neurons with spike adaptation. The dependence of this state on the different network parameters is investigated, and it is shown that this mechanism is robust against inhomogeneities, sparseness of the connectivity, and noise. In networks of two populations, one excitatory and one inhibitory, we show that decreasing the inhibitory feedback can cause the network to switch from a tonically active, asynchronous state to the synchronized bursting state. Finally, we show that the same mechanism also causes synchronized burst activity in networks of more realistic conductance-based model neurons.

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Xie, Xurong, Xunying Liu, Tan Lee y Lan Wang. "Bayesian Learning for Deep Neural Network Adaptation". IEEE/ACM Transactions on Audio, Speech, and Language Processing 29 (2021): 2096–110. http://dx.doi.org/10.1109/taslp.2021.3084072.

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Patre, P. M., S. Bhasin, Z. D. Wilcox y W. E. Dixon. "Composite Adaptation for Neural Network-Based Controllers". IEEE Transactions on Automatic Control 55, n.º 4 (abril de 2010): 944–50. http://dx.doi.org/10.1109/tac.2010.2041682.

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Yu, D. L. y T. K. Chang. "Adaptation of diagonal recurrent neural network model". Neural Computing and Applications 14, n.º 3 (23 de marzo de 2005): 189–97. http://dx.doi.org/10.1007/s00521-004-0453-9.

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Joty, Shafiq, Nadir Durrani, Hassan Sajjad y Ahmed Abdelali. "Domain adaptation using neural network joint model". Computer Speech & Language 45 (septiembre de 2017): 161–79. http://dx.doi.org/10.1016/j.csl.2016.12.006.

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Denker, John S. "Neural network models of learning and adaptation". Physica D: Nonlinear Phenomena 22, n.º 1-3 (octubre de 1986): 216–32. http://dx.doi.org/10.1016/0167-2789(86)90242-3.

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YAEGER, LARRY S. "IDENTIFYING NEURAL NETWORK TOPOLOGIES THAT FOSTER DYNAMICAL COMPLEXITY". Advances in Complex Systems 16, n.º 02n03 (mayo de 2013): 1350032. http://dx.doi.org/10.1142/s021952591350032x.

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We use an ecosystem simulator capable of evolving arbitrary neural network topologies to explore the relationship between an information theoretic measure of the complexity of neural dynamics and several graph theoretical metrics calculated for the underlying network topologies. Evolutionary trends confirm and extend previous results demonstrating an evolutionary selection for complexity and small-world network properties during periods of behavioral adaptation. The resultant mapping of the space of network topologies occupied by the most complex networks yields new insights into the relationship between network structure and function. The highest complexity networks are found within limited numerical ranges of clustering coefficient, characteristic path length, small-world index, and global efficiency. The widths of these ranges vary from quite narrow to modest, and provide a guide to the most productive regions of the space of neural topologies in which to search for complexity. Our demonstration that evolution selects for complex dynamics and small-world networks helps explain biological evidence for these trends and provides evidence for selection of these characteristics based purely on network function—with no physical constraints on network structure—thus suggesting that functional and structural evolutionary pressures cooperate to produce brains optimized for adaptation to a complex, variable world.

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Ziemke, Tom. "Radar Image Segmentation Using Self-Adapting Recurrent Networks". International Journal of Neural Systems 08, n.º 01 (febrero de 1997): 47–54. http://dx.doi.org/10.1142/s0129065797000070.

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This paper presents a novel approach to the segmentation and integration of (radar) images using a second-order recurrent artificial neural network architecture consisting of two sub-networks: a function network that classifies radar measurements into four different categories of objects in sea environments (water, oil spills, land and boats), and a context network that dynamically computes the function network's input weights. It is shown that in experiments (using simulated radar images) this mechanism outperforms conventional artificial neural networks since it allows the network to learn to solve the task through a dynamic adaptation of its classification function based on its internal state closely reflecting the current context.

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Li, Xiaofeng, Suying Xiang, Pengfei Zhu y Min Wu. "Establishing a Dynamic Self-Adaptation Learning Algorithm of the BP Neural Network and Its Applications". International Journal of Bifurcation and Chaos 25, n.º 14 (30 de diciembre de 2015): 1540030. http://dx.doi.org/10.1142/s0218127415400301.

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In order to avoid the inherent deficiencies of the traditional BP neural network, such as slow convergence speed, that easily leading to local minima, poor generalization ability and difficulty in determining the network structure, the dynamic self-adaptive learning algorithm of the BP neural network is put forward to improve the function of the BP neural network. The new algorithm combines the merit of principal component analysis, particle swarm optimization, correlation analysis and self-adaptive model, hence can effectively solve the problems of selecting structural parameters, initial connection weights and thresholds and learning rates of the BP neural network. This new algorithm not only reduces the human intervention, optimizes the topological structures of BP neural networks and improves the network generalization ability, but also accelerates the convergence speed of a network, avoids trapping into local minima, and enhances network adaptation ability and prediction ability. The dynamic self-adaptive learning algorithm of the BP neural network is used to forecast the total retail sale of consumer goods of Sichuan Province, China. Empirical results indicate that the new algorithm is superior to the traditional BP network algorithm in predicting accuracy and time consumption, which shows the feasibility and effectiveness of the new algorithm.

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Tesis sobre el tema "Neural network adaptation"

Donati, Lorenzo. "Domain Adaptation through Deep Neural Networks for Health Informatics". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14888/.

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The PreventIT project is an EU Horizon 2020 project aimed at preventing early functional decline at younger old age. The analysis of causal links between risk factors and functional decline has been made possible by the cooperation of several research institutes' studies. However, since each research institute collects and delivers different kinds of data in different formats, so far the analysis has been assisted by expert geriatricians whose role is to detect the best candidates among hundreds of fields and offer a semantic interpretation of the values. This manual data harmonization approach is very common in both scientific and industrial environments. In this thesis project an alternative method for parsing heterogeneous data is proposed. Since all the datasets represent semantically related data, being all made from longitudinal studies on aging-related metrics, it is possible to train an artificial neural network to perform an automatic domain adaptation. To achieve this goal, a Stacked Denoising Autoencoder has been implemented and trained to extract a domain-invariant representation of the data. Then, from this high-level representation, multiple classifiers have been trained to validate the model and ultimately to predict the probability of functional decline of the patient. This innovative approach to the domain adaptation process can provide an easy and fast solution to many research fields that now rely on human interaction to analyze the semantic data model and perform cross-dataset analysis. Functional decline classifiers show a great improvement in their performance when trained on the domain-invariant features extracted by the Stacked Denoising Autoencoder. Furthermore, this project applies multiple deep neural network classifiers on top of the Stacked Denoising Autoencoder representation, achieving excellent results for the prediction of functional decline in a real case study that involves two different datasets.

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Haskey, Stephen. "A modified One-Class-One-Network ANN architecture for dynamic phoneme adaptation". Thesis, Loughborough University, 1998. https://dspace.lboro.ac.uk/2134/12099.

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As computers begin to pervade aspects of our everyday lives, so the problem of communication from man-to-machine becomes increasingly evident. In recent years, there has been a concerted interest in speech recognition offering a user to communicate freely with a machine. However, this deceptively simple means for exchanging information is in fact extremely complex. A single utterance can contain a wealth of varied information concerning the speaker's gender, age, dialect and mood. Numerous subtle differences such as intonation, rhythm and stress further add to the complexity, increasing the variability between inter- and intra-speaker utterances. These differences pose an enormous problem, especially for a multi-user system since it is impractical to train for every variation of every utterance from every speaker. Consequently adaptation is of great importance, allowing a system with limited knowledge to dynamically adapt towards a new speakers characteristics. A new modified artificial neural network (ANN) was proposed incorporating One-Class-OneNetwork (OCON) subnet architectures connected via a common front-end adaptation layer. Using vowel phonemes from the TIMIT speech database, the adaptation was concentrated on neurons within the front-end layer, resulting in only information common to all classes, primarily speaker characteristics, being adapted. In addition, this prevented new utterances from interfering with phoneme unique information in the corresponding OCON subnets. Hence a more efficient adaptation procedure was created which, after adaptation towards a single class, also aided in the recognition of the remaining classes within the network. Compared with a conventional multi-layer perceptron network, results for inter- and intraspeaker adaptation showed an equally marked improvement for the recognition of adapted phonemes during both full neuron and front-layer neuron adaptation within the new modified architecture. When testing the effects of adaptation on the remaining unadapted vowel phonemes, the modified architecture (allowing only the neurons in the front-end layer to adapt) yielded better results than the modified architecture allowing full neuron adaptation. These results highlighted the storing of speaker information, common to all classes, in the front-end layer allowing efficient inter- and intra-speaker dynamic adaptation.

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Wen, Tsung-Hsien. "Recurrent neural network language generation for dialogue systems". Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/275648.

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Language is the principal medium for ideas, while dialogue is the most natural and effective way for humans to interact with and access information from machines. Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact on usability and perceived quality. Many commonly used NLG systems employ rules and heuristics, which tend to generate inflexible and stylised responses without the natural variation of human language. However, the frequent repetition of identical output forms can quickly make dialogue become tedious for most real-world users. Additionally, these rules and heuristics are not scalable and hence not trivially extensible to other domains or languages. A statistical approach to language generation can learn language decisions directly from data without relying on hand-coded rules or heuristics, which brings scalability and flexibility to NLG. Statistical models also provide an opportunity to learn in-domain human colloquialisms and cross-domain model adaptations. A robust and quasi-supervised NLG model is proposed in this thesis. The model leverages a Recurrent Neural Network (RNN)-based surface realiser and a gating mechanism applied to input semantics. The model is motivated by the Long-Short Term Memory (LSTM) network. The RNN-based surface realiser and gating mechanism use a neural network to learn end-to-end language generation decisions from input dialogue act and sentence pairs; it also integrates sentence planning and surface realisation into a single optimisation problem. The single optimisation not only bypasses the costly intermediate linguistic annotations but also generates more natural and human-like responses. Furthermore, a domain adaptation study shows that the proposed model can be readily adapted and extended to new dialogue domains via a proposed recipe. Continuing the success of end-to-end learning, the second part of the thesis speculates on building an end-to-end dialogue system by framing it as a conditional generation problem. The proposed model encapsulates a belief tracker with a minimal state representation and a generator that takes the dialogue context to produce responses. These features suggest comprehension and fast learning. The proposed model is capable of understanding requests and accomplishing tasks after training on only a few hundred human-human dialogues. A complementary Wizard-of-Oz data collection method is also introduced to facilitate the collection of human-human conversations from online workers. The results demonstrate that the proposed model can talk to human judges naturally, without any difficulty, for a sample application domain. In addition, the results also suggest that the introduction of a stochastic latent variable can help the system model intrinsic variation in communicative intention much better.

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Gangireddy, Siva Reddy. "Recurrent neural network language models for automatic speech recognition". Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28990.

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The goal of this thesis is to advance the use of recurrent neural network language models (RNNLMs) for large vocabulary continuous speech recognition (LVCSR). RNNLMs are currently state-of-the-art and shown to consistently reduce the word error rates (WERs) of LVCSR tasks when compared to other language models. In this thesis we propose various advances to RNNLMs. The advances are: improved learning procedures for RNNLMs, enhancing the context, and adaptation of RNNLMs. We learned better parameters by a novel pre-training approach and enhanced the context using prosody and syntactic features. We present a pre-training method for RNNLMs, in which the output weights of a feed-forward neural network language model (NNLM) are shared with the RNNLM. This is accomplished by first fine-tuning the weights of the NNLM, which are then used to initialise the output weights of an RNNLM with the same number of hidden units. To investigate the effectiveness of the proposed pre-training method, we have carried out text-based experiments on the Penn Treebank Wall Street Journal data, and ASR experiments on the TED lectures data. Across the experiments, we observe small but significant improvements in perplexity (PPL) and ASR WER. Next, we present unsupervised adaptation of RNNLMs. We adapted the RNNLMs to a target domain (topic or genre or television programme (show)) at test time using ASR transcripts from first pass recognition. We investigated two approaches to adapt the RNNLMs. In the first approach the forward propagating hidden activations are scaled - learning hidden unit contributions (LHUC). In the second approach we adapt all parameters of RNNLM.We evaluated the adapted RNNLMs by showing the WERs on multi genre broadcast speech data. We observe small (on an average 0.1% absolute) but significant improvements in WER compared to a strong unadapted RNNLM model. Finally, we present the context-enhancement of RNNLMs using prosody and syntactic features. The prosody features were computed from the acoustics of the context words and the syntactic features were from the surface form of the words in the context. We trained the RNNLMs with word duration, pause duration, final phone duration, syllable duration, syllable F0, part-of-speech tag and Combinatory Categorial Grammar (CCG) supertag features. The proposed context-enhanced RNNLMs were evaluated by reporting PPL and WER on two speech recognition tasks, Switchboard and TED lectures. We observed substantial improvements in PPL (5% to 15% relative) and small but significant improvements in WER (0.1% to 0.5% absolute).

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Tomashenko, Natalia. "Speaker adaptation of deep neural network acoustic models using Gaussian mixture model framework in automatic speech recognition systems". Thesis, Le Mans, 2017. http://www.theses.fr/2017LEMA1040/document.

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Les différences entre conditions d'apprentissage et conditions de test peuvent considérablement dégrader la qualité des transcriptions produites par un système de reconnaissance automatique de la parole (RAP). L'adaptation est un moyen efficace pour réduire l'inadéquation entre les modèles du système et les données liées à un locuteur ou un canal acoustique particulier. Il existe deux types dominants de modèles acoustiques utilisés en RAP : les modèles de mélanges gaussiens (GMM) et les réseaux de neurones profonds (DNN). L'approche par modèles de Markov cachés (HMM) combinés à des GMM (GMM-HMM) a été l'une des techniques les plus utilisées dans les systèmes de RAP pendant de nombreuses décennies. Plusieurs techniques d'adaptation ont été développées pour ce type de modèles. Les modèles acoustiques combinant HMM et DNN (DNN-HMM) ont récemment permis de grandes avancées et surpassé les modèles GMM-HMM pour diverses tâches de RAP, mais l'adaptation au locuteur reste très difficile pour les modèles DNN-HMM. L'objectif principal de cette thèse est de développer une méthode de transfert efficace des algorithmes d'adaptation des modèles GMM aux modèles DNN. Une nouvelle approche pour l'adaptation au locuteur des modèles acoustiques de type DNN est proposée et étudiée : elle s'appuie sur l'utilisation de fonctions dérivées de GMM comme entrée d'un DNN. La technique proposée fournit un cadre général pour le transfert des algorithmes d'adaptation développés pour les GMM à l'adaptation des DNN. Elle est étudiée pour différents systèmes de RAP à l'état de l'art et s'avère efficace par rapport à d'autres techniques d'adaptation au locuteur, ainsi que complémentaire
Differences between training and testing conditions may significantly degrade recognition accuracy in automatic speech recognition (ASR) systems. Adaptation is an efficient way to reduce the mismatch between models and data from a particular speaker or channel. There are two dominant types of acoustic models (AMs) used in ASR: Gaussian mixture models (GMMs) and deep neural networks (DNNs). The GMM hidden Markov model (GMM-HMM) approach has been one of the most common technique in ASR systems for many decades. Speaker adaptation is very effective for these AMs and various adaptation techniques have been developed for them. On the other hand, DNN-HMM AMs have recently achieved big advances and outperformed GMM-HMM models for various ASR tasks. However, speaker adaptation is still very challenging for these AMs. Many adaptation algorithms that work well for GMMs systems cannot be easily applied to DNNs because of the different nature of these models. The main purpose of this thesis is to develop a method for efficient transfer of adaptation algorithms from the GMM framework to DNN models. A novel approach for speaker adaptation of DNN AMs is proposed and investigated. The idea of this approach is based on using so-called GMM-derived features as input to a DNN. The proposed technique provides a general framework for transferring adaptation algorithms, developed for GMMs, to DNN adaptation. It is explored for various state-of-the-art ASR systems and is shown to be effective in comparison with other speaker adaptation techniques and complementary to them

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Buttar, Sarpreet Singh. "Applying Artificial Neural Networks to Reduce the Adaptation Space in Self-Adaptive Systems : an exploratory work". Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-87117.

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Self-adaptive systems have limited time to adjust their configurations whenever their adaptation goals, i.e., quality requirements, are violated due to some runtime uncertainties. Within the available time, they need to analyze their adaptation space, i.e., a set of configurations, to find the best adaptation option, i.e., configuration, that can achieve their adaptation goals. Existing formal analysis approaches find the best adaptation option by analyzing the entire adaptation space. However, exhaustive analysis requires time and resources and is therefore only efficient when the adaptation space is small. The size of the adaptation space is often in hundreds or thousands, which makes formal analysis approaches inefficient in large-scale self-adaptive systems. In this thesis, we tackle this problem by presenting an online learning approach that enables formal analysis approaches to analyze large adaptation spaces efficiently. The approach integrates with the standard feedback loop and reduces the adaptation space to a subset of adaptation options that are relevant to the current runtime uncertainties. The subset is then analyzed by the formal analysis approaches, which allows them to complete the analysis faster and efficiently within the available time. We evaluate our approach on two different instances of an Internet of Things application. The evaluation shows that our approach dramatically reduces the adaptation space and analysis time without compromising the adaptation goals.

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Palapelas, Kantola Philip. "Extreme Quantile Estimation of Downlink Radio Channel Quality". Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177657.

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The application area of Fifth Generation New Radio (5G-NR) called Ultra-Reliable and Low-Latency Communication (URLLC) requires a reliability, the probability of receiving and decoding a data packet correctly, of 1 - 10^5. For this requirement to be fulfilled in a resource-efficient manner, it is necessary to have a good estimation of extremely low quan- tiles of the channel quality distribution, so that appropriate resources can be distributed to users of the network system. This study proposes and evaluates two methods for estimating extreme quantiles of the downlink channel quality distribution, linear quantile regression and Quantile Regression Neural Network (QRNN). The models were trained on data from Ericsson’s system-level radio network simulator, and evaluated on goodness of fit and resourcefulness. The focus of this study was to estimate the quantiles 10^2, 10^3 and 10^4 of the distribution. The results show that QRNN generally performs better than linear quantile regression in terms of pseudoR2, which indicates goodness of fit, when the sample size is larger. How- ever, linear quantile regression was more effective for smaller sample sizes. Both models showed difficulty estimating the most extreme quantiles. The less extreme quantile to esti- mate, the better was the resulting pseudoR2-score. For the largest sample size, the resulting pseudoR2-scores of the QRNN was 0.20, 0.12 and 0.07, and the scores of linear quantile regression was 0.16, 0.10 and 0.07 for the respective quantiles 10^2, 10^3 and 10^4. It was shown that both evaluated models were significantly more resourceful than us- ing the average of the 50 last measures of channel quality subtracted with a fixed back-off value as a predictor. QRNN had the most optimistic predictions. If using the QRNN, theo- retically, on average 43% more data could be transmitted while fulfilling the same reliability requirement than by using the fixed back-off value.

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Fic, Miloslav. "Adaptace parametrů ve fuzzy systémech". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221163.

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This Master’s thesis deals with adaptation of fuzzy system parameters with main aim on artificial neural network. Current knowledge of methods connecting fuzzy systems and artificial neural networks is discussed in the search part of this work. The search in Student’s works is discussed either. Chapter focused on methods application deals with classifying ability verification of the chosen fuzzy-neural network with Kohonen learning algorithm. Later the model of fuzzy system with parameters adaptation based on fuzzyneural network with Kohonen learning algorithm is shown.

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Vu, Hien Duc. "Adaptation des méthodes d'apprentissage automatique pour la détection de défauts d'arc électriques". Electronic Thesis or Diss., Université de Lorraine, 2019. http://docnum.univ-lorraine.fr/ulprive/DDOC_T_2019_0152_VU.pdf.

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La détection des arcs électriques se produisant dans un réseau électrique par des approches d’apprentissage représente le cœur des travaux exposés dans cette thèse. Le problème a d’abord été vu comme la classification de séries temporelles à taille fixe en deux classes: normal et défaut. Cette première partie s’appuie sur les travaux de la littérature où les algorithmes de détection sont organisés principalement sur une étape de transformation des signaux acquis sur le réseau, suivie d’une étape d’extraction de caractéristiques descriptives et enfin d’une étape de décision. L’approche multicritères adoptée ici a pour objectif de répondre aux imprécisions systématiquement constatées. Une méthodologie de sélection des meilleures combinaisons et de transformation et de descripteurs a été proposée en exploitant des solutions d’apprentissage. La mise au point de descripteurs pertinents étant toujours difficile, l'évaluation des possibilités offertes par l'apprentissage profond a également été étudiée. Dans une seconde phase l’étude a porté sur les aspects variables dans le temps de la détection de défaut. Deux voies statistiques de décision ont été explorées l’une basée sur le test de probabilités séquentiel (SPRT) l’autre basée sur les réseaux de neurones artificiels LSTM (Long Short Time Memory Network) chacune de ces deux méthodes exploite à sa manière la durée d’une première étape de classification comprise entre 0 et 1 (normal, défaut). La décision par SPRT utilise une intégration de la classification initiale. LSTM apprend à classer des données à temps variable. Les résultats du réseau LSTM sont très prometteurs, il reste néanmoins quelques points à approfondir. L’ensemble de ces travaux s’appuie sur des expérimentations avec des données les plus complètes et les plus large possible sur le domaine des réseaux alternatifs 230V dans un contexte domestique et industriel. La précision obtenue approche les 100% dans la majorité des situations
The detection of electric arcs occurring in an electrical network by machine learning approaches represents the heart of the work presented in this thesis. The problem was first considered as a classification of fixed-size time series with two classes: normal and default. This first part is based on the work of the literature where the detection algorithms are organized mainly on a step of the transformation of the signals acquired on the network, followed by a step of extraction of descriptive characteristics and finally a step of decision. The multi-criteria approach adopted here aims to respond to systematic classification errors. A methodology for selecting the best combinations, transformation, and descriptors has been proposed by using learning solutions. As the development of relevant descriptors is always difficult, differents solutions offered by deep learning has also been studied. In a second phase, the study focused on the variable aspects in time of the fault detection. Two statistical decision paths have been explored, one based on the sequential probabilistic test (SPRT) and the other based on artificial neural networks LSTM (Long Short Time Memory Network). Each of these two methods exploits in its way the duration a first classification step between 0 and 1 (normal, default). The decision by SPRT uses an integration of the initial classification. LSTM learns to classify data with variable time. The results of the LSTM network are very promising, but there are a few things to explore. All of this work is based on experiments with the most complete and broadest possible data on the field of 230V alternative networks in a domestic and industrial context. The accuracy obtained is close to 100% in the majority of situations

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Ainapure, Abhijeet Narhar. "Application and Performance Enhancement of Intelligent Cross-Domain Fault Diagnosis in Rotating Machinery". University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623164772153736.

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Libros sobre el tema "Neural network adaptation"

Lee, Tsu-Chang. Structure level adaptation for artificial neural networks. Boston: Kluwer Academic Publishers, 1991.

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Lee, Tsu-Chang. Structure Level Adaptation for Artificial Neural Networks. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-3954-4.

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Lee, Tsu-Chang. Structure Level Adaptation for Artificial Neural Networks. Boston, MA: Springer US, 1991.

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Stonier, Russel J. y Xing Huo Yu. Complex systems: Mechanism of adaptation. Amsterdam: IOS Press, 1994.

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Neuronal adaptation theory: Including 29 exercises with solutions, 43 essential ideas, and 108 partially couloured figures, experiment explanations, and general theorems. Frankfurt am Main: Peter Lang, 1996.

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1931-, Haykin Simon S., ed. Kalman filtering and neural networks. New York: Wiley, 2001.

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J, Stonier Russel y Xing Huo-yu, eds. Complex systems: Mechanism of adaptation. Amsterdam: IOS Press, 1994.

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Focus, Symposium on Learning and Adaptation in Stochastic and Statistical Systems (2001 Baden-Baden Germany). Proceedings of the Focus Symposium on Learning and Adaptation in Stochastic and Statistical Systems. Windsor, Ont: International Institute for Advanced Studies in Systems Research and Cybernetics, 2002.

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Marcello, Pucci y Vitale Gianpaolo, eds. Power converters and AC electrical drives with linear neutral networks. Boca Raton: CRC Press, 2012.

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Channel-Mismatch Compensation in Speaker Identification Feature Selection and Adaptation with Artificial Neural Networks. Storming Media, 1998.

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Capítulos de libros sobre el tema "Neural network adaptation"

Ljung, L., J. Sjöberg y H. Hjalmarsson. "On Neural Network Model Structures in System Identification". En Identification, Adaptation, Learning, 366–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03295-4_9.

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Cai, ManJun, JinCun Liu, GuangJun Tian, XueJian Zhang y TiHua Wu. "Hybrid Neural Network Controller Using Adaptation Algorithm". En Advances in Neural Networks – ISNN 2007, 148–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72383-7_19.

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Patil, Dipali Himmatrao y Amit Gadekar. "Tuberculosis Detection Using a Deep Neural Network". En Proceedings in Adaptation, Learning and Optimization, 600–608. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31164-2_51.

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Hozjan, Tomaž, Goran Turk y Iztok Fister. "Hybrid Artificial Neural Network for Fire Analysis of Steel Frames". En Adaptation, Learning, and Optimization, 149–69. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14400-9_7.

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Kursin, Andrei. "Neural Network: Input Anticipation May Lead to Advanced Adaptation Properties". En Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003, 779–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44989-2_93.

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Lee, Tsu-Chang. "Application Example: An Adaptive Neural Network Source Coder". En Structure Level Adaptation for Artificial Neural Networks, 135–53. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-3954-4_5.

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Vidyasagar, M. "An Overview of Computational Learning Theory and Its Applications to Neural Network Training". En Identification, Adaptation, Learning, 400–422. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03295-4_10.

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Yang, Yongxin y Timothy M. Hospedales. "Unifying Multi-domain Multitask Learning: Tensor and Neural Network Perspectives". En Domain Adaptation in Computer Vision Applications, 291–309. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58347-1_16.

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Zajíc, Zbyněk, Jan Zelinka, Jan Vaněk y Luděk Müller. "Convolutional Neural Network for Refinement of Speaker Adaptation Transformation". En Speech and Computer, 161–68. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11581-8_20.

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Bureš, Tomáš, Petr Hnětynka, Martin Kruliš, František Plášil, Danylo Khalyeyev, Sebastian Hahner, Stephan Seifermann, Maximilian Walter y Robert Heinrich. "Attuning Adaptation Rules via a Rule-Specific Neural Network". En Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning, 215–30. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19759-8_14.

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Actas de conferencias sobre el tema "Neural network adaptation"

Li, Jinyu, Jui-Ting Huang y Yifan Gong. "Factorized adaptation for deep neural network". En ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854662.

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Jae Hoon Jeong y Soo-Young Lee. "Speaker adaptation based on judge network with small adaptation words". En Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. IEEE, 2000. http://dx.doi.org/10.1109/ijcnn.2000.859377.

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Steffens Henrique, Alisson, Vinicius Almeida dos Santos y Rodrigo Lyra. "NEAT Snake: a both evolutionary and neural network adaptation approach". En 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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Wu, Chunwei, Guitao Cao, Wenming Cao, Hong Wang y He Ren. "Debiased Prototype Network for Adversarial Domain Adaptation". En 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533346.

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Vesely, Karel, Shinji Watanabe, Katerina Zmolikova, Martin Karafiat, Lukas Burget y Jan Honza Cernocky. "Sequence summarizing neural network for speaker adaptation". En 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7472692.

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Patre, Parag M., Shubhendu Bhasin, Zachary D. Wilcox y Warren E. Dixon. "Composite adaptation for neural network-based controllers". En 2009 Joint 48th IEEE Conference on Decision and Control (CDC) and 28th Chinese Control Conference (CCC). IEEE, 2009. http://dx.doi.org/10.1109/cdc.2009.5400453.

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Ma, Min, Michael Nirschl, Fadi Biadsy y Shankar Kumar. "Approaches for Neural-Network Language Model Adaptation". En Interspeech 2017. ISCA: ISCA, 2017. http://dx.doi.org/10.21437/interspeech.2017-1310.

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Kimoto, T., Y. Yaginuma, S. Nagata y K. Asakawa. "Inverse modeling of dynamical system-network architecture with identification network and adaptation network". En 1991 IEEE International Joint Conference on Neural Networks. IEEE, 1991. http://dx.doi.org/10.1109/ijcnn.1991.170460.

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Szekely, Geza y Thomas Lindblad. "Parameter adaptation in a simplified pulse-coupled neural network". En Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Neural Networks Fuzzy Systems, Evolutionary Systems and Virtual Re, editado por Thomas Lindblad, Mary Lou Padgett y Jason M. Kinser. SPIE, 1999. http://dx.doi.org/10.1117/12.343046.

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Muniz, L. F., C. N. Lintzmayer, C. Jutten y D. G. Fantinato. "Neuroevolutive Strategies for Topology and Weights Adaptation of Artificial Neural Networks". En Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/kdmile.2022.227807.

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Among the methods for training Multilayer Perceptron networks, backpropagation is one of the most used ones on problems of supervised learning. However, it presents some limitations, such as local convergence and the a priori choice of the network topology. Another possible approach for training is to use Genetic Algorithms to optimize the weights and topology of networks, which is known as neuroevolution. In this work, we compare the efficiency of training and defining topology with a modified neuroevolution approach using two different metaheuristics with backpropagation on 5 classification problems. The network’s efficiency is assessed through Mutual Information and Information plane. We concluded that neuroevolution found simpler topologies, while backpropagation showed higher efficiency at updating the weights.

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Informes sobre el tema "Neural network adaptation"

Miles, Gaines E., Yael Edan, F. Tom Turpin, Avshalom Grinstein, Thomas N. Jordan, Amots Hetzroni, Stephen C. Weller, Marvin M. Schreiber y Okan K. Ersoy. Expert Sensor for Site Specification Application of Agricultural Chemicals. United States Department of Agriculture, agosto de 1995. http://dx.doi.org/10.32747/1995.7570567.bard.

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In this work multispectral reflectance images are used in conjunction with a neural network classifier for the purpose of detecting and classifying weeds under real field conditions. Multispectral reflectance images which contained different combinations of weeds and crops were taken under actual field conditions. This multispectral reflectance information was used to develop algorithms that could segment the plants from the background as well as classify them into weeds or crops. In order to segment the plants from the background the multispectrial reflectance of plants and background were studied and a relationship was derived. It was found that using a ratio of two wavelenght reflectance images (750nm and 670nm) it was possible to segment the plants from the background. Once ths was accomplished it was then possible to classify the segmented images into weed or crop by use of the neural network. The neural network developed for this work is a modification of the standard learning vector quantization algorithm. This neural network was modified by replacing the time-varying adaptation gain with a constant adaptation gain and a binary reinforcement function. This improved accuracy and training time as well as introducing several new properties such as hill climbing and momentum addition. The network was trained and tested with different wavelength combinations in order to find the best results. Finally, the results of the classifier were evaluated using a pixel based method and a block based method. In the pixel based method every single pixel is evaluated to test whether it was classified correctly or not and the best weed classification results were 81% and its associated crop classification accuracy is 57%. In the block based classification method, the image was divided into blocks and each block was evaluated to determine whether they contained weeds or not. Different block sizes and thesholds were tested. The best results for this method were 97% for a block size of 8 inches and a pixel threshold of 60. A simulation model was developed to 1) quantify the effectiveness of a site-specific sprayer, 2) evaluate influence of diffeent design parameters on efficiency of the site-specific sprayer. In each iteration of this model, infected areas (weed patches) in the field were randomly generated and the amount of required herbicides for spraying these areas were calculated. The effectiveness of the sprayer was estimated for different stain sizes, nozzle types (conic and flat), nozzle sizes and stain detection levels of the identification system. Simulation results indicated that the flat nozzle is much more effective as compared to the conic nozzle and its relative efficiency is greater for small nozzle sizes. By using a site-specific sprayer, the average ratio between the spraying areas and the stain areas is about 1.1 to 1.8 which can save up to 92% of herbicides, especially when the proportion of the stain areas is small.

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Kosko, Bart. Stability and Adaptation of Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, noviembre de 1990. http://dx.doi.org/10.21236/ada230108.

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Yatsymirska, Mariya. KEY IMPRESSIONS OF 2020 IN JOURNALISTIC TEXTS. Ivan Franko National University of Lviv, marzo de 2021. http://dx.doi.org/10.30970/vjo.2021.50.11107.

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The article explores the key vocabulary of 2020 in the network space of Ukraine. Texts of journalistic, official-business style, analytical publications of well-known journalists on current topics are analyzed. Extralinguistic factors of new word formation, their adaptation to the sphere of special and socio-political vocabulary of the Ukrainian language are determined. Examples show modern impressions in the media, their stylistic use and impact on public opinion in a pandemic. New meanings of foreign expressions, media terminology, peculiarities of translation of neologisms from English into Ukrainian have been clarified. According to the materials of the online media, a «dictionary of the coronavirus era» is provided. The journalistic text functions in the media on the basis of logical judgments, credible arguments, impressive language. Its purpose is to show the socio-political problem, to sharpen its significance for society and to propose solutions through convincing considerations. Most researchers emphasize the influential role of journalistic style, which through the media shapes public opinion on issues of politics, economics, education, health care, war, the future of the country. To cover such a wide range of topics, socio-political vocabulary is used first of all – neutral and emotionally-evaluative, rhetorical questions and imperatives, special terminology, foreign words. There is an ongoing discussion in online publications about the use of the new foreign token «lockdown» instead of the word «quarantine», which has long been learned in the Ukrainian language. Research on this topic has shown that at the initial stage of the pandemic, the word «lockdown» prevailed in the colloquial language of politicians, media personalities and part of society did not quite understand its meaning. Lockdown, in its current interpretation, is a restrictive measure to protect people from a dangerous virus that has spread to many countries; isolation of the population («stay in place») in case of risk of spreading Covid-19. In English, US citizens are told what a lockdown is: «A lockdown is a restriction policy for people or communities to stay where they are, usually due to specific risks to themselves or to others if they can move and interact freely. The term «stay-at-home» or «shelter-in-place» is often used for lockdowns that affect an area, rather than specific locations». Content analysis of online texts leads to the conclusion that in 2020 a special vocabulary was actively functioning, with the appropriate definitions, which the media described as a «dictionary of coronavirus vocabulary». Media broadcasting is the deepest and pulsating source of creative texts with new meanings, phrases, expressiveness. The influential power of the word finds its unconditional embodiment in the media. Journalists, bloggers, experts, politicians, analyzing current events, produce concepts of a new reality. The world is changing and the language of the media is responding to these changes. It manifests itself most vividly and emotionally in the network sphere, in various genres and styles.

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Seginer, Ido, Louis D. Albright y Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, febrero de 2001. http://dx.doi.org/10.32747/2001.7575271.bard.

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Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, control, structure and crop). 2. Using these models, develop algorithms for an early detection of deviations from the normal. 3. Develop identifying procedures for the most important faults. 4. Develop accommodation procedures while awaiting a repair. The Technion team focused on the shoot environment and the Cornell University team focused on the root environment. Achievements Models: Accurate models were developed for both shoot and root environment in the greenhouse, utilizing neural networks, sometimes combined with robust physical models (hybrid models). Suitable adaptation methods were also successfully developed. The accuracy was sufficient to allow detection of frequently occurring sensor and equipment faults from common measurements. A large data base, covering a wide range of weather conditions, is required for best results. This data base can be created from in-situ routine measurements. Detection and isolation: A robust detection and isolation (formerly referred to as 'identification') method has been developed, which is capable of separating the effect of faults from model inaccuracies and disturbance effects. Sensor and equipment faults: Good detection capabilities have been demonstrated for sensor and equipment failures in both the shoot and root environment. Water stress detection: An excitation method of the shoot environment has been developed, which successfully detected water stress, as soon as the transpiration rate dropped from its normal level. Due to unavailability of suitable monitoring equipment for the root environment, crop faults could not be detected from measurements in the root zone. Dust: The effect of screen clogging by dust has been quantified. Implications Sensor and equipment fault detection and isolation is at a stage where it could be introduced into well equipped and maintained commercial greenhouses on a trial basis. Detection of crop problems requires further work. Dr. Peleg was primarily responsible for developing and implementing the innovative data analysis tools. The cooperation was particularly enhanced by Dr. Peleg's three summer sabbaticals at the ARS, Northem Plains Agricultural Research Laboratory, in Sidney, Montana. Switching from multi-band to hyperspectral remote sensing technology during the last 2 years of the project was advantageous by expanding the scope of detected plant growth attributes e.g. Yield, Leaf Nitrate, Biomass and Sugar Content of sugar beets. However, it disrupted the continuity of the project which was originally planned on a 2 year crop rotation cycle of sugar beets and multiple crops (com and wheat), as commonly planted in eastern Montana. Consequently, at the end of the second year we submitted a continuation BARD proposal which was turned down for funding. This severely hampered our ability to validate our findings as originally planned in a 4-year crop rotation cycle. Thankfully, BARD consented to our request for a one year extension of the project without additional funding. This enabled us to develop most of the methodology for implementing and running the hyperspectral remote sensing system and develop the new analytical tools for solving the non-repeatability problem and analyzing the huge hyperspectral image cube datasets. However, without validation of these tools over a ful14-year crop rotation cycle this project shall remain essentially unfinished. Should the findings of this report prompt the BARD management to encourage us to resubmit our continuation research proposal, we shall be happy to do so.

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