Download or read book Neural Networks with Dynamic Synapses for Times Series Prediction written by Tomasz J. Cholewo and published by . This book was released on 1998 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book TIME SERIES FORECASTING USING NEURAL NETWORKS EXAMPLES WITH MATLAB written by Cesar Perez Lopez and published by CESAR PEREZ. This book was released on with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB has the tool Deep Leraning Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, timeseries forecasting, and dynamic system modeling and control. Dynamic neural networks are good at timeseries prediction. You can use the Neural Net Time Series app to solve different kinds of time series problems It is generally best to start with the GUI, and then to use the GUI to automatically generate command line scripts. Before using either method, the first step is to define the problem by selecting a data set. Each GUI has access to many sample data sets that you can use to experiment with the toolbox. If you have a specific problem that you want to solve, you can load your own data into the workspace. With MATLAB is possibe to solve three different kinds of time series problems. In the first type of time series problem, you would like to predict future values of a time series y(t) from past values of that time series and past values of a second time series x(t). This form of prediction is called nonlinear autoregressive network with exogenous (external) input, or NARX. In the second type of time series problem, there is only one series involved. The future values of a time series y(t) are predicted only from past values of that series. This form of prediction is called nonlinear autoregressive, or NAR. The third time series problem is similar to the first type, in that two series are involved, an input series (predictors) x(t) and an output series (responses) y(t). Here you want to predict values of y(t) from previous values of x(t), but without knowledge of previous values of y(t). This book develops methods for time series forecasting using neural networks across MATLAB

Download or read book Neural Networks as Forecasting Experts written by Rajendra B. Patil and published by . This book was released on 1990 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Learning to Predict in Networks with Heterogeneous and Dynamic Synapses written by Daniel Burnham and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A salient difference between artificial and biological neural networks is the complexity and diversity of individual units in the latter (Tasic et al., 2018). This remarkable diversity is present in the cellular and synaptic dynamics. In this study we focus on the role in learning of one such dynamical mechanism missing from most artificial neural network models, short-term synaptic plasticity (STSP). Biological synapses have dynamics over at least two time scales: a long time scale, which maps well to synaptic changes in artificial neural networks during learning, and the short time scale of STSP, which is typically ignored. Recent studies have shown the utility of such short-term dynamics in a variety of tasks (Masse et al., 2019; Perez-Nieves et al., 2021), and networks trained with such synapses have been shown to better match recorded neuronal activity and animal behavior (Hu et al., 2020). Here, we allow the timescale of STSP in individual neurons to be learned, simultaneously with standard learning of overall synaptic weights. We study learning performance on two predictive tasks, a simple dynamical system and a more complex MNIST pixel sequence. When the number of computational units is similar to the task dimensionality, RNNs with STSP outperform standard RNN and LSTM models. A potential explanation for this improvement is the encoding of activity history in the short-term synaptic dynamics, a biological form of long short-term memory. Beyond a role for synaptic dynamics themselves, we find a reason and a role for their diversity: learned synaptic time constants become heterogeneous across training and contribute to improved prediction performance in feedforward architectures. These results demonstrate how biologically motivated neural dynamics improve performance on the fundamental task of predicting future inputs with limited computational resources, and how learning such predictions drives neural dynamics towards the diversity found in biological brains.

Download or read book Recurrent Neural Networks for Short Term Load Forecasting written by Filippo Maria Bianchi and published by Springer. This book was released on 2017-11-09 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt: The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system. Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementations of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures. Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. This work performs a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. The authors test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. The text also provides a general overview of the most important architectures and defines guidelines for configuring the recurrent networks to predict real-valued time series.

Download or read book Neural Information Processing written by Akira Hirose and published by Springer. This book was released on 2016-09-30 with total page 646 pages. Available in PDF, EPUB and Kindle. Book excerpt: The four volume set LNCS 9947, LNCS 9948, LNCS 9949, and LNCS 9950 constitutes the proceedings of the 23rd International Conference on Neural Information Processing, ICONIP 2016, held in Kyoto, Japan, in October 2016. The 296 full papers presented were carefully reviewed and selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysis; neural data analysis; robotics and control; bio-inspired/energy efficient information processing; whole brain architecture; neurodynamics; bioinformatics; biomedical engineering; data mining and cybersecurity workshop; machine learning; neuromorphic hardware; sensory perception; pattern recognition; social networks; brain-machine interface; computer vision; time series analysis; data-driven approach for extracting latent features; topological and graph based clustering methods; computational intelligence; data mining; deep neural networks; computational and cognitive neurosciences; theory and algorithms.

Download or read book Dynamic Activity Predictions Using Graph based Neural Networks for Time Series Forecasting written by Bhuvan Kumar Chennoju and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Time series forecasting is a vital task in numerous fields, and traditional methods, machine learning models, and neural graph networks have been employed to improve prediction accuracy. However, these techniques need to be revised to understand interdependencies and establishing long-term dependencies when dealing with a network of time series, such as predicting energy demand on interconnected grids. To tackle these challenges, this thesis introduces a framework implementing Attention-based Temporal Graph Convolutional Networks (ATGCNs) that enables holistic treatment of a group of time series while learning inter-dependencies and facilitating message passing for enhanced model efficiency. The major contribution of this thesis lies in developing graph embedding algorithms that convert Microbusiness density data into graph data, considering the spatial distance and time series for the proposed ATGCNs model, enabling dynamic activity predictions. The proposed framework is evaluated through experiments using a U.S. Microbusiness density dataset from the GoDaddy Open Survey. The results reveal that ATGCNs outperform traditional time series statistics and machine learning methods in various evaluation metrics, demonstrating comparable forecasting performance to conventional time series forecasting while addressing network scalability and dynamic nature. Additionally, real-time prediction visualizations based on Tableau were developed to showcase the dynamic nature of predictions in the U.S. Microbusiness density domain. In conclusion, this study’s findings highlight the potential advantages of employing graph-based neural networks for time series forecasting, suggesting that incorporating additional data sources could improve prediction accuracy. As future work, transfer learning with ATGCNs will be applied to new domains such as climate prediction or energy demand on interconnected grids. Furthermore, the graph-embedding algorithm and visualization techniques developed in this project will be applied to new domains and datasets across different domains.

Download or read book Time Series Analysis with Neural Networks Examples Across MATLAB written by C. PEREZ and published by Independently Published. This book was released on 2019-04-12 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB has the tool Neural Network Toolbox (Deep Learning Toolbox from version 18) that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control.The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox.This book develops, through examples, the possibilities of working with neural networks to model and predict with time series.

Download or read book Chaotic Time Series Prediction Using Artificial Neural Networks written by and published by . This book was released on 1991 with total page 6 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper describes the use of artificial neural networks to model the complex oscillations defined by a chaotic Verhuist animal population dynamic. A predictive artificial neural network model is developed and tested, and results of computer simulations are given. These results show that the artificial neural network model predicts the chaotic time series with various initial conditions, growth parameters, or noise.

Download or read book Time Series Forecasting Using Dynamic Particle Swarm Optimizer Trained Neural Networks written by Salihu Aish Abdulkarim and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Time series forecasting is a very important research area because of its practical application in many elds. Due to the importance of time series forecasting, much research e ort has gone into the development of forecasting models and in improving prediction accuracies. The interest in using arti cial neural networks (NNs) to model and forecast time series has been growing. The most popular type of NN is arguably the feedforward NN (FNN). FNNs have structures capable of learning static input-output mappings, suitable for prediction of non-linear stationary time series. To model nonstationary time series, recurrent NNs (RNNs) are often used. The recurrent/delayed connections in RNNs give the network dynamic properties to e ectively handle temporal sequences. These recurent/delayed connections, however, increase the number of weights that are required to be optimized during training of the NN. Particle swarm optimization (PSO) is an e cient population based search algorithm based on the social dynamics of group interactions in bird ocks. Several studies have applied PSO to train NNs for time series forecasting, and the results indicated good performance on stationary time series, and poor performance on non-stationary and highly noisy time series. These studies have assumed static environments, making the original PSO, which was designed for static environments, unsuitable for training NNs for forecasting many real-world time series generated by non-stationary processes. In dealing with non-stationary data, modi ed versions of PSOs for optimization in dynamic environments are used. These dynamic PSOs are yet to be applied to train NNs on forecasting problems. The rst part of this thesis formulates training of a FNN forecaster as a dynamic optimization problem, to investigate the application of a dynamic PSO algorithm to train FNNs in forecasting time series in non-stationary environments. For this purpose, a set of experiments were conducted on ten forecasting problems under nine di erent dynamic scenarios. Results obtained are compared to the results of FNNs trained using a standard PSO and resilient backpropagation (RPROP). The results show that the dynamic PSO algorithm outperform the PSO and RPROP algorithms. These ndings highlight the potential of using dynamic PSO in training FNNs for real-world forecasting applications. The second part of the thesis tests the hypothesis that recurrent/delayed connections are not necessary if a dynamic PSO is used as the training algorithm. For this purpose, set of experiments were carried out on the same problems and under the same dynamic scenarios. Each experiment involves training a FNN using a dynamic PSO algorithm, and comparing the result to that obtained from four di erent types of RNNs (i.e. Elman NN, Jordan NN, Multi-Recurrent NN and Time Delay NN), each trained separately using RPROP, standard PSO and the dynamic PSO algorithm. The results show that the FNNs trained with the dynamic PSO signi cantly outperform all the RNNs trained using any of the algorithms considered. These ndings show that recurrent/delayed connections are not necessary in NNs used for time series forecasting (for the time series considered in this study) as long as a dynamic PSO algorithm is used as the training method.

Download or read book Combining Dynamic Factor Models and Artificial Neural Networks in Time Series Forecasting with Applications written by Ali Basher Abd Allah Babikir and published by . This book was released on 2014 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Temporal neural networks for treating time variant series written by and published by . This book was released on 1904 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: As RNA Temporais, em função de sua estrutura, consideram o tempo na sua operação, incorporando memória de curto prazo distribuída na rede em todos os neurônios escondidos e em alguns dos casos nos neurônios de saída. Esta classe de redes é utilizada para representar melhor a natureza temporal dos sistemas dinâmicos. Em contraste, a RNA estática tem uma estrutura apropriada para tarefas de reconhecimento de padrões, classificação e outras de natureza estática ou estacionária tendo sido utilizada com sucesso em diversas aplicações. O objetivo desta tese, portanto foi estudar a teoria e avaliar o desempenho das Redes Neurais Temporais em comparação com as Redes Neurais Estáticas, em aplicações de sistemas dinâmicos. O desenvolvimento desta pesquisa envolveu 3 etapas principais: pesquisa bibliográfica das metodologias desenvolvidas para RNA Temporais; seleção e implementação de modelos para a avaliação destas redes; e estudo de casos. A pesquisa bibliográfica permitiu compila e classificar os principais trabalhos sobre RNA Temporais. Tipicamente, estas redes podem ser classificadas em dois grupos: Redes com Atraso no Tempo e Redes Recorrentes. Para a análise de desempenho, selecionou-se uma redee de cada grupo para implementação. Do primeiro grupo foi selecionada a Rede FIR, onde as sinapses são filtros FIR (Finite-duration Impulse Response) que representam a natureza temporal do problema. A rede FIR foi selecionada por englobar praticamente, todos os outros métodos de sua classe e apresentar um modelo matemático mais formal. Do segundo grupo, considerou-se a rede recorrente de Elman que apresenta realimentação global de cada um dos neurônios escondidos para todos eles. No estudo de casos testou-se o desempenho das redes selecionadas em duas linhas de aplicação: previsão de séries temporais e processamento digital de sinais. No caso de previsão de séries temporais, foram utilizadas séries de consumo de energia elétrica, comparando-se os resultados com os encontrados na literatura a partir de métodos de Holt-Winters, Box & Jenkins e RNA estáticas. No caso da aplicação das RNA em processamento digital de sinais, utilizou-se a filtragem de ruído em sinais de voz onde foram feitas comparações com os resultados apresentados pelo filtro neural convencional, que é uma rede feed-forward multicamada com o algoritmo de retropropagação para o aprendizado. Este trabalho demonstrou na prática que as RNA temporais conseguem capturar as características dos processos temporais de forma mais eficiente que as RNA Estatísticas e outros métodos tradicionais, podendo aprender diretamente o comportamento não estacionário das séries temporais. Os resultados demonstraram que a rede neural FIR e a rede Elman aprendem melhor a complexidade dos sinais de voz.

Download or read book Time Series Analysis Using Neural Networks written by Ritu Vijay and published by LAP Lambert Academic Publishing. This book was released on 2012-08 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural networks are suitable for many tasks in pattern recognition and machine learning. Unlike conventional techniques for time series analysis, an artificial neural network needs little information about the time series data and can be applied to a broad range of problems. The usage of artificial neural networks for time series analysis relies purely on the data that were observed. As Radial Basis networks with one hidden layer is capable of approximating any measurable function. An artificial neural network is powerful enough to represent any form of time series. The capability to generalize allows artificial neural networks to learn even in the case of noisy and/or missing data. Another advantage over linear models is the network's ability to represent nonlinear time series. Prediction of tides is very much essential for human activities and to reduce the construction cost in marine environment. This book presents an application of the artificial neural network with Radial basis function for accurate prediction of tides. This neural network model predicts the time series data of hourly tides directly while using an an efficient learning process.

Download or read book Time Series Prediction Using Neural Networks written by Worapoj Kreesuradej and published by . This book was released on 1993 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Time Series Prediction Using Neural Networks written by Vikas V. Agnihotri and published by . This book was released on 1995 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Recurrent Neural Networks for Time Series Prediction written by Ebtesam Shenouda Tanyous and published by . This book was released on 1996 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Time Series Prediction Using Adaptive Hierarchical Neural Networks written by Karsten Schierholt and published by . This book was released on 1996 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: