Scientists, engineers and the like are a strange lot. Unperturbed by societal norms,
they direct their energies to finding better alternatives to existing theories and concocting
solutions to unsolved problems. Driven by an insatiable curiosity, they record
their observations and crunch the numbers. This tome is about the science of crunching.
It’s about digging out something of value from the detritus that others tend to
leave behind. The described approaches involve constructing models to process the
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Decentralized Approach for Nonlinear Prediction of Time Series Data in Sensor Networks
This volume brings about the contemporary results in the field of discrete-time systems. It covers papers written on the topics of robust control, nonlinear systems and recent applications. Although the technical views are different, they all geared towards focusing on the up-to-date knowledge gain by the researchers and providing effective developments along the systems and control arena. Each topic has a detailed discussions and suggestions for future perusal by interested investigators.
The Extended Kalman Filter (EKF) provides an efﬁcient method for generating approximate maximum-likelihood estimates of the state of a discrete-time nonlinear dynamical system (see Chapter 1). The ﬁlter involves a recursive procedure to optimally combine noisy observations with predictions from the known dynamic model. A second use of the EKF involves estimating the parameters of a model (e.g., neural network) given clean training data of input and output data (see Chapter 2).
Neural Networks as Nonlinear Adaptive Filters
Neural networks, in particular recurrent neural networks, are cast into the framework of nonlinear adaptive ﬁlters. In this context, the relation between recurrent neural networks and polynomial ﬁlters is ﬁrst established. Learning strategies and algorithms are then developed for neural adaptive system identiﬁers and predictors. Finally, issues concerning the choice of a neural architecture with respect to the bias and variance of the prediction performance are discussed....
Some Practical Considerations of Predictability and Learning Algorithms for Various Signals
In this chapter, predictability, detecting nonlinearity and performance with respect to the prediction horizon are considered. Methods for detecting nonlinearity of signals are ﬁrst discussed. Then, diﬀerent algorithms are compared for the prediction of nonlinear and nonstationary signals, such as real NO2 air pollutant and heart rate variability signals, together with a synthetic chaotic signal.
Data-Reusing Adaptive Learning Algorithms
In this chapter, a class of data-reusing learning algorithms for recurrent neural networks is analysed. This is achieved starting from a case of feedforward neurons, through to the case of networks with feedback, trained with gradient descent learning algorithms. It is shown that the class of data-reusing algorithms outperforms the standard (a priori ) algorithms for nonlinear adaptive ﬁltering in terms of the instantaneous prediction error.
DUAL EXTENDED KALMAN FILTER METHODS
Eric A. Wan and Alex T. Nelson
Department of Electrical and Computer Engineering, Oregon Graduate Institute of Science and Technology, Beaverton, Oregon, U.S.A.
5.1 INTRODUCTION The Extended Kalman Filter (EKF) provides an efﬁcient method for generating approximate maximum-likelihood estimates of the state of a discrete-time nonlinear dynamical system (see Chapter 1). The ﬁlter involves a recursive procedure to optimally combine noisy observations with predictions from the known dynamic model.
EVALUATING THE PREDICTIVE ACCURACY OF MODELS
1. Introduction 2. Numerical solution of nonlinear models 3. Evaluation of ex ante forecasts 4. Evaluation of ex post forecasts 5. An alternative method for evaluating predictive accuracy 6. Conclusion References
This paper contributes to the literature on modeling the behavior
of the futures basis on several fronts.
Specifically, the paper investi-
gates nonlinearities in basis adjustment toward its equilibrium value
and proposes a novel approach to modeling the behavior of the basis
inspired by the prediction of the theoretical arguments mentioned
above. Using data for the S&P 500 and the FTSE 100 indices during
the post-crash period since 1988, the authors provide strong evidence of
nonlinear mean reversion in the futures basis for both indices consid-