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Analog computational circuits have been demonstrated to provide substantial improvements in power and speed relative to digital circuits, especially for applications requiring extreme parallelism but only modest precision. Deep machine learning is one such area and stands to benefit greatly from analog and mixed-signal implementations. However, even at modest precisions, offsets and non-linearity...
Genetic regulatory networks have emerged as a useful way to elucidate the biochemical pathways for biological functions. Yet, determination of the exact parametric forms for these models remain a major challenge. In this paper, we present a novel computational approach implemented in C++ to solve this inverse problem. This takes the form of an optimization stage first after which Bayesian filtering...
This paper introduces a major new cross-disciplinary research project that looks at the UK health and social care system, as part of an ambitious, broader initiative to apply methods from complexity science to a range of key global challenges. This particular project aims to develop new, integrated models for the supply and demand of both health and social care, in the context of the societal change...
Based on the GM(1, 1) theory, this paper studies the deficiency in power load forecasting. combining the regression model, the paper proprose a new method to forecast the power load. In this paper, the grey model GM(1, 1) is hybridized into the regression model. This results in grey regression model, which is explained detailedly in succession. Based on an example, the basic grey model and grey regression...
In developing a stock price forecasting model, the first step is usually feature extraction. Nonlinear independent component analysis (NLICA) is a novel feature extraction technique to find independent sources given only observed data that are mixtures of the unknown sources, without prior knowledge of the mixing mechanisms. It assumes that the observed mixtures are the nonlinear combination of latent...
Multi-variable grey dynamic forecasting model is a main model of grey systems theory. In this paper, we constructed the discrete grey model of multi-variables. We contrasted the model with GM (n, h) model and the result showed two models are equal to each other through data transformation. Then we could build the bridge of discrete grey model and traditional grey model. Based on this conclusion we...
In this study, the application of independent component analysis (ICA), a new feature extraction method, and support vector regression (SVR) in time series prediction is presented. The proposed method first use ICA as preprocessing to transform the input space composed of original time series data into the feature space consisting of independent components (ICs) representing underlying information/features...
Abstract A new method of using Vis/NIR spectroscopy technique to identify the varieties of red wines was studied. Through comparing modeling performance built by different amounts of independent components, 20 independent components (ICs) extracted by independent components analysis (ICA) were employed as the inputs of the BP neural networks and were consider to be important parameter for calibration...
A nonlinear model representation consisting of an interpolation of several local models, which are valid within certain operation regimes, is proposed. Using this representation, first principles models and black-box models like neural networks may be integrated. Only operation regimes of the plant not adequately modeled by first principles are being represented and learned by a neural network memory...
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