This paper investigates how to develop a learning-based demand response approach for electric water heater in a smart home that can minimize the energy cost of the water heater while meeting the comfort requirements of energy consumers. First, a learning-based, data-driven model of an electric water heater is developed by using a nonlinear autoregressive network with external input (NARX) using...

A hybrid recurrent neural network is shown to learn small initial mealy machines (that can be thought of as translation machines translating input strings to corresponding output strings, as opposed to recognition automata that classify strings as either grammatical or nongrammatical) from positive training samples. A well-trained neural net 1 is then presented once again with the training set ...

Lake water level is an essential indicator of environmental changes caused by natural and human factors. The Poyang Lake, the largest freshwater lake in China, has exhibited a dramatic variation for past few years, especially after completion Three Gorges Dam (TGD). However, there lack more accurate assessment effect TGD on (PLWL) at finer temporal scales (e.g., daily scale). Here, we used thre...

The wing rock motion is mathematically described by a nonlinear differential equation with coefficients varying with angle of attack. In this paper, a neural-network-based adaptive control system is developed for the wing rock motion control. The adaptive controller comprises a neural network controller and a compensation controller. The neural network controller using a recurrent neural networ...

Volumetric imaging of samples using fluorescence microscopy plays an important role in various fields including physical, medical and life sciences. Here we report a deep learning-based volumetric image inference framework that uses 2D images are sparsely captured by standard wide-field microscope at arbitrary axial positions within the sample volume. Through recurrent convolutional neural netw...

in the present study iran’s rice imports trend is forecasted, using artificial neural networks and econometric methods, during 2009 to 2013, and their results are compared. the results showed that feet forward neural network leading with less forecast error and had better performance in comparison to econometric techniques and also, other methods of neural networks, such as recurrent networks a...