Có 12+ tài liệu thuộc chủ đề "dự đoán mạng lưới"
tailieu.vn Xem trực tuyến Tải xuống
Recurrent Neural Networks for Prediction Authored by Danilo P. Artificial neural network (ANN) models have been extensively studied with the aim of achieving human-like performance, especially in the field of pattern recognition.. He proposed some of the basic concepts such as that memory is composed of simple elements connected to each other via a number of different mechanisms (Medler 1998).....
tailieu.vn Xem trực tuyến Tải xuống
There are many reasons for this, foremost amongst these is that adaptive filtering, prediction or identification do not require explicit a priori statistical knowledge of the input data.. an error calculation block (the difference between the desired response and the output of the filter structure);. a control (learning) algorithm for the adaptation of the weights.. The type of learning represented...
tailieu.vn Xem trực tuyến Tải xuống
Such filters have immedi- ate application in the prediction of discrete time random signals that arise from some. The neuron, or node, is the basic processing element within a neural network. Such feedback can either be local to the neurons or global to the network (Haykin 1999b. When the inputs to a neural network are delayed versions of a discrete...
tailieu.vn Xem trực tuyến Tải xuống
Activation Functions Used in Neural Networks. The choice of nonlinear activation function has a key influence on the complexity and performance of artificial neural networks, note the term neural network will be used interchangeably with the term artificial neural network. From these universal approximation properties, we then demonstrate the need for a sigmoidal activation function within a neuron. For rigour,...
tailieu.vn Xem trực tuyến Tải xuống
Recurrent Neural Networks Architectures. Finally, further discussion of recurrent neural network architectures is provided.. By system we consider the actual underlying physics 2 that generate the data, whereas by model we consider a mathematical description of the system. 1 System identification, for instance, consists of choice of the model, model parameter estimation and model validation.. Figure 5.1 Effects of y...
tailieu.vn Xem trực tuyến Tải xuống
Neural Networks as Nonlinear Adaptive Filters. Finally, issues concerning the choice of a neural architecture with respect to the bias and variance of the prediction performance are discussed.. Pearson (1995), in his article on nonlinear input–output modelling, shows that block oriented nonlinear models are a subset of the class of Volterra models. In the previous chapter, we have shown that...
tailieu.vn Xem trực tuyến Tải xuống
This enables derivation of the asymptotic stability (AS) and global asymptotic stability (GAS) criteria for neural relaxive systems. Stability and convergence are key issues in the analysis of dynamical adaptive sys- tems, since the analysis of the dynamics of an adaptive system can boil down to the discovery of an attractor (a stable equilibrium) or some other kind of fixed...
tailieu.vn Xem trực tuyến Tải xuống
Data-Reusing Adaptive Learning Algorithms. In this chapter, a class of data-reusing learning algorithms for recurrent neural net- works is analysed. It is shown that the class of data-reusing algorithms outperforms the standard (a priori) algorithms for nonlinear adaptive filtering in terms of the instanta- neous prediction error. The relationships between the a priori and a posteriori errors, learning rate and...
tailieu.vn Xem trực tuyến Tải xuống
A normalised version of the real-time recurrent learning (RTRL) algorithm is intro- duced. This has been achieved via local linearisation of the RTRL around the current point in the state space of the network. Such an algorithm provides an adaptive learn- ing rate normalised by the L 2 norm of the gradient vector at the output neuron. In the area...
tailieu.vn Xem trực tuyến Tải xuống
Convergence of Online Learning Algorithms in Neural Networks. 10.1 Perspective. Using the assump- tion of contractivity of the activation function of a neuron and relaxing the rigid assumptions of the fixed optimal weights of the system, the analysis presented is gen- eral and is applicable to a wide range of existing algorithms. It is shown that some of the results...
tailieu.vn Xem trực tuyến Tải xuống
11.1 Perspective. 11.2 Introduction. The performance of these models can then determine whether more flexible nonlinear models are necessary to capture the underlying structure of the signal. Penalised likelihood methods such as AIC or BIC (Box and Jenkins 1976) exist for choosing the order of the autoregressive model to be fitted to the data. (b) The ACF of the NO...
tailieu.vn Xem trực tuyến Tải xuống
12.1 Perspective. 12.2 Introduction. The gradient ∇ W E(k) in (12.2) comprises the first derivative of the nonlinear activation function (12.1), which is a function of β (Narendra and Parthasarathy 1990). For instance, for a simple nonlinear FIR filter shown in Figure 12.1, the weight update is given by. (12.3) For a function Φ(β, x. Φ(βx), which is the case...