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Contingency planning is the first stage in developing a formal set of production planning and control activities for the reuse of products obtained via return flows in a closed-loop supply chain. The paper takes a contingency approach to explore the factors that impact production planning and control for closed-loop supply chains that incorporate product(More)
This paper introduces reservoir computing for static pattern recognition. Reservoir computing networks are neural networks with a sparsely connected recurrent hidden layer (or reservoir) of neurons. The weights from the inputs to the reservoir and the reservoir weights are randomly selected. The weights of the second layer are determined with a linear(More)
We introduce Augmented Efficient BackProp as a strategy for applying the backpropagation algorithm to deep autoencoders, i.e., autoassociators with many hidden layers, without relying on a weight initialization using restricted Boltzmann machines. This training method is an extension of Efficient BackProp, first proposed by LeCun et al. [1], and is(More)
The purpose of this paper is to evaluate and benchmark ensemble methods for time series prediction for daily currency exchange rates using ensemble feedforward neural networks and kernel partial least squares (K-PLS). Best-practice forecasting methods for the US Dollar (USD) per Indian Rupee (IR) are applied for training, validating, and testing the machine(More)
We introduce Augmented Efficient BackProp, a strategy for applying the backpropagation algorithm to deep autoencoders, i.e. autoassociators with many hidden layers, without relying on a weight initialization using restricted Boltzmann machines (RBMs). This training method, benchmarked on three different types of application datasets, is an extension of(More)