In this paper, we explore different conceptualizations for representing neighborhoods in residential location choice models, and describe three alternative ways for constructing operational units to represent neighborhoods. In particular, we examine the possibility of using the census units to represent the hierarchical ‘fixed neighborhood’ definition, and the circular units and network bands t...

In this paper, we explore different conceptualizations to represent neighborhoods in residential location choice models, and describe three alternative ways for constructing operational units to represent neighborhoods. In particular, we examine the possibility of using the census units to represent the hierarchical ‘fixed neighborhood’ definition, and the circular units and network bands to re...

We propose a process model for hierarchical perceptual sound organization, which recognizes perceptual sounds included in incoming sound signals. We consider perceptual sound organization as a scene analysis problem in the auditory domain. Our model consists of multiple processing modules and a hypothesis network for quantitative integration of multiple sources of information. When input inform...

One of the most important concerns of reverse logistics network design is to locate interacting facilities in an efficient and cost effective manner, which forms a typical hierarchical system with multiple layer configuration. Considering the hierarchical relationship and flow of waste among the different facility types, both single-flow pattern and multi-flow pattern are discussed in this pape...

The Self-Organizing Map is a very popular unsupervised neural network model for the analysis of high-dimensional input data as in data mining applications. However, at least two limitations have to be noted, which are caused, on the one hand, by the static architecture of this model, as well as, on the other hand, by the limited capabilities for the representation of hierarchical relations of t...

The Self-Organizing Map is a very popular unsupervised neural network model for the analysis of high-dimensional input data as in data mining applications. However, at least two limitations have to be noted, which are caused, on the one hand, by the static architecture of this model, as well as, on the other hand, by the limited capabilities for the representation of hierarchical relations of t...

The self-organizing map (SOM) is a very popular unsupervised neural-network model for the analysis of high-dimensional input data as in data mining applications. However, at least two limitations have to be noted, which are related to the static architecture of this model as well as to the limited capabilities for the representation of hierarchical relations of the data. With our novel growing ...

In optimal stopping problems, people are asked to choose the maximum out of a sequence of values, under the constraint that a number can only be chosen when it is presented. We present a series of threshold models of human decision making on optimal stopping problems, including a new hierarchical model that assumes individual differences in threshold setting are controlled by deviations or bias...

(ABSTRACT) Situation (al) awareness (SA) is critical to analyze, predict and perform tasks effectively in a dynamic environment. Many studies on SA have ignored network dynamism and its effect on SA, focusing on simple environments. Many studies involving the network and SA have refrained from attempting to model information space dynamism (i.e. dynamic scenarios which may have more than one pr...

A nonlinear mathematical model of a feed-batch fermentation process of Bacillus thuringiensis (Bt.), is derived. The obtained model is validated by experimental data. Identification and direct adaptive neural control systems with and without integral term are proposed. The system contains a neural identifier and a neural controller, based on the recurrent trainable neural network model. The app...