This chapter introduces the methodology of particle swarm optimization algorithm usage as a tool for finding customer profiles based on a previously developed predictive model that predicts events like selection of some products or services with some probabilities. Particle swarm optimization algorithm is used as a tool that finds optimal values of input variables within developed predictive mo...

Trained neural network models, which exhibit features of neural activity recorded from behaving animals, may provide insights into the circuit mechanisms of cognitive functions through systematic analysis of network activity and connectivity. However, in contrast to the graded error signals commonly used to train networks through supervised learning, animals learn from reward feedback on defini...

T his research is an attempt to determine the agronomic plan, shadow price and final value with an emphasis on optimization of water use in five main agricultural regions of Kerman province (Kerman, Baft, Bardsir, Bam, and Jiroft) and five crops (wheat, barley, potato, onion, and tomato). Analysis was based on three scenarios: (i) current planting conditions, (ii) profit maximization...

ÐStandard, exact techniques based on likelihood maximization are available for learning Auto-Regressive Process models of dynamical processes. The uncertainty of observations obtained from real sensors means that dynamics can be observed only approximately. Learning can still be achieved via aEM-KoÐExpectation-Maximization (EM) based on Kalman Filtering. This cannot handle more complex dynamics...

This paper describes an algorithm to cluster and segment sequences of low-level user actions into sequences of distinct high-level user tasks. The algorithm uses text contained in interface windows as evidence of the state of user–computer interaction. Window text is summarized using latent semantic indexing (LSI). Hierarchical models are built using expectation–maximization to represent users ...

Continuous time modified Cox-Ingersoll-Ross (1985) stochastic model is employed, combining with Hamilton (1989) type Markov regime switching framework, to study daily foreign exchange rates, where all parameter values depend on the value of a continuous time Markov chain. The Expectation-Maximization algorithm is extended, generalized, applied to a more general class of regime switching models ...

Beach recreation values are often needed by policy-makers and resource managers to efficiently manage coastal resources, especially in popular coastal areas like Southern California. This article presents welfare values derived from random utility maximization-based recreation demand models that explain an individual’s decisions about whether or not to visit a beach and which beach to visit. Th...

We present a method for robust input design for nonlinear state-space models. The method optimizes a scalar cost function of the Fisher information matrix over a set of marginal distributions of stationary processes. By using elements from graph theory we characterize such a set. Since the true system is unknown, the resulting optimization problem takes the uncertainty on the true value of the ...