Job-shop scheduling is an important task for manufacturing industries. We are interested in the particular task of scheduling payload processing for NASA's space shuttle program. This paper summarizes our previous work on formulating this task for solution by the reinforcement learning algorithm T D(). A shortcoming of this previous work was its reliance on hand-engineered input features. This ...

A quantitative procedure was developed to predict the composition of ternary ground spice mixtures using an electronic nose. Basil, cinnamon, and garlic were mixed in different compositions and presented to an enose. Nineteen training mixtures were used to build predictive models. Model performance was tested using 5 other mixtures. Three neural network structures—multilayer perceptron (MLP), M...

This paper concerns dynamic neural networks for signal processing: architectural issues are considered but the paper focuses on learning algorithms that work on-line. Locally recurrent neural networks, namely MLP with IIR synapses and generalization of Local Feedback MultiLayered Networks (LF MLN), are compared to more traditional neural networks, i.e. static MLP with input and/or output buffer...

Human eye movement modelling is a new, challenging, and promising research topic in computer vision. Human eye movement modelling aims at simulating the scan path in which a human being views an image, scene, or video. The successful modelling of human eye movements potentially benefits a wide range of applications such as image retrieval, image annotation, medical image diagnosis, and human vi...

Rural–urban immigration, regional wars, refugees, and natural disasters all bring to prominence the importance of studying urban growth. Increased growth rates are becoming a global phenomenon creating stress on agricultural land, spreading pollution, accelerating warming, increasing water run-off, which adds exponentially pressure resources impacts climate change. Based integration machine lea...

The NARX network is a dynamical neural architecture commonly used for inputoutput modeling of nonlinear dynamical systems. When applied to time series prediction, the NARX network is designed as a feedforward Time Delay Neural Network (TDNN), i.e. without the feedback loop of delayed outputs, reducing substantially its predictive performance. In this paper, we show that the original architectur...

In recent years, the integration of Statistical Process Control (SPC) and Engineering Process Control (EPC) has received considerable attentions due to the superiority for the process improvement. However, a drawback of the integration of SPC and EPC may be encountered for monitoring a process. Because EPC would compensate for the effects of underlying disturbances, the disturbance patterns cou...

Research for advanced traveler information systems (ATIS) has been focused on urban roads. However, research for short-term traffic prediction on all categories of highways is needed, as highway agencies expect to implement intelligent transportation systems across their jurisdictions. In this study, genetic algorithms were used to design time delay neural network (TDNN) models as well as local...

In this paper we present NPen ++, a connectionist system for writer independent, large vocabulary on-line cursive handwriting recognition. This system combines a robust input representation, which preserves the dynamic writing information, with a neural network architecture, a so called Multi-State Time Delay Neural Network (MS-TDNN), which integrates rec.ognition and segmentation in a single f...

Machine learning techniques are applicable to computer system optimization. We show that shared memory multiprocessors can successfully utilize machine learning algorithms for memory access pattern prediction. In particular three different on-line machine learning prediction techniques were tested to learn and predict repetitive memory access patterns for three typical parallel processing appli...