2 Quantization of Feed-forward Networks A feed-forward neural network consists of a finite set of neurons x1, . . . , xk par- titioned into a sequence of layers: an input layer with n neurons, followed by one or many hidden layers, finally followed by an output layer with m neurons....
low-carbon city pilot policy; haze pollution; quasi-natural experiment; DID model; spatial spillover effect1. Introduction As a result of the surge in energy consumption triggered by the rapid development of China’s urbanization and industry in recent decades, the air quality of cities has ...
Liu et al. [57] added an artificial neural network (ANN) with one hidden layer to the LSTM. In LSTM, the variation in travel time between bus stops over time was considered, whereas the ANN captured the spatial features via the speed of the bus route link. Additionally, Tang et al. ...
Effect of Pore Size Distribution on Compressive Behavior of Moderately Expanded Low-Density Polyethylene Foams A pure-linear activation function is used in all models in the output layer and Levenberg-Marquardt algorithm (More, 1978) is utilized for training the networks. PERFORMANCE COMPARISON OF ANFI...
5.1. Implementing the Neural Network Our model consists of a simple two-layer neural network: one hidden layer and one output layer. The input layer will take features from the Iris dataset, including measurements such as sepal length, sepal width, and petal length. ...