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Cognition and control processes share many similar characteristics, and decisionmaking and learning under the paradigm of multiple models has increasingly gained attention in both fields. The controlled finite Markov chain (CFMC) approach enables to deal with a large variety of signals and systems with multivariable, nonlinear, and stochastic nature. In this article, adaptive control based on multiple models is considered. For a set of candidate plant models, CFMC models (and controllers) are constructed off-line. The outcomes of the CFMC models are compared with frequentist information obtained from on-line data. The best model (and controller) is chosen based on the Kullback–Leibler information. This approach to adaptive control emphasizes the use of physical models as the basis of reliable plant identification. Three series of simulations are conducted: to examine the performace of the developed Matlab-tools; to illustrate the approach in the control of a nonlinear nonminimum phase van der Vusse CSTR plant; and to examine the suggested model selection method for the adaptive control.

Polynomial artificial neural networks (PANN) have been shown to be powerful for forecasting nonlinear time series. The training time is small compared to the time used by other algorithms of artificial neural networks and the capacity to compute relations between the inputs and outputs represented by every term of the polynomial. In this paper a new structure of polynomial is presented that improves the performance of this type of network considering only non-integers exponents. The architecture adaptation uses genetic algorithm (GA) to find the optimal architecture for every example. Some examples of sunspots and chaotic time series are presented.

The goal of this paper is to design a new control algorithm for open-loop control of complex systems. This control approach is based on a genealogical decision tree for both regulation and tracking control problems. The idea behind this control strategy consists of associating Gaussian distributions to both the norms of the control actions and the tracking errors. This stochastic search model can be interpreted as a simple genetic particle evolution model with a natural birth and death interpretation. It converges on probability. A numerical example dealing with the control of a fluidized bed combustion power plant illustrates the feasibility and the performance of this control algorithm.

Identification and predictive control using a probabilistic CMC-ARX model is considered. The control problem is solved by maximizing the confidence in that the selected action results in a minimum cost. Bounds for the change in confidence of the optimal control action are derived, and a learning control algorithm is then suggested.

This paper concerns process modelling using fuzzy neural networks. In distributed logic processors (DLP) the rule base is parameterised. The DLP derivatives required by gradient-based training methods are given, and the recursive prediction error method is used to adjust the model parameters. The power of the approach is illustrated with a modelling example where NOx-emission data from a full-scale fluidised-bed combustion district heating plant are used. The method presented in this paper is general, and can be applied to other complex processes as well.

Pulp and paper mills can be seen as big pumping plants, where mass is pumped from one step to another. The proper operation of the process in its different stages does not allow large deviations from given operation conditions, which makes it essential to monitor and control the flow rate and the consistency of the pulp. A structure is suggested for the modelling of pulp flow rate, and possibilities for using the pump-valve system as a flow meter are examined. The overall model structure consists of a Wiener model for pressure difference, a non-linear dynamic valve model, and a static mapping for flow rate. A full-scale pilot plant of pulp flow through a valve in a pipeline is used for experimentation. Linear dynamics and non-linearities due to the pump, valve and the pulp consistency are identified based on online measurements obtained from calibration tests. The results show that a simple model could be identified which, together with a valve model, can be further applied for the purposes of accurate flow control.

This paper considers the use of adaptive learning systems for multimodal functions optimization and process control. The learning system collects and processes the available data to achieve the desired control objective. The environment where the automaton operates corresponds to the function to be optimized (the process to be controlled) which is assumed to be unknown function of a single parameter x. The admissible region of x (control variable) is quantized into N levels. These levels are associated with the actions of the automaton. The set of these actions is further decomposed into nonempty subsets. The action set is changing from instant to instant. At each time an action set is selected according to a probability distribution. The action and the action set probabilities are adjusted using learning algorithms (reinforcement schemes). This optimization and control approach is tested using two numerical examples (multimodal function and a chemical reactor). However, the concept can be applied to other examples as well. Simulation results illustrate the feasibility and the performance of this adaptive automaton.

A new algorithm for training the parameters of a distributed logic processor is suggested, based on learning automata. A fuzzy inference system is implemented on a multilayer perceptron platform. Various possibilities for assigning learning automata are discussed and two assignment strategies are proposed. The behaviour of the resulting algorithm is illustrated using flue-gas NO/sub x/ emission data measured from an industrial-size fluidised-bed combustor.

This paper concerns the adaptive selection of the optimal order of linear regression models using a variable-structure stochastic learning automaton. The Alaike criterion is derived for stationary and non-stationary cases, and it is shown that the optimal order minimizes a loss function corresponding to the evaluation of this criterion. The order of the regression model belongs to a finite set. Each order value is associated with an action of the automaton. The Bush-Mosteller reinforcement scheme with normalized automaton input is used to adjust the probability distribution. Simulation results illustrate the feasibility and performance of this model order selection approach

The adaptation made for the Polynomial Artificial Neural Networks (PANN) using not only integer exponentials but also fractional exponentials, have shown evidence of its better performance, especially, when it works with non-linear and chaotic time series. In this paper we show the comparison of the PANN improved model of fractional exponentials with a new logarithmic model. We show that this new model have even better performance than the last PANN improved model.

Student's studies can be seen as consisting of chains or sequences of courses, the learning activities of a student along these lines make his study path. In this paper, methods for detection, analysis and control of learning activities are developed, based on students' credit record data. A central aim of the work is to support the supervision of students using currently available data bases.

Publisher Summary Artificial intelligence is the science of intelligence and is connected to systems sciences through the basic assumptions of computational psychology. Computational intelligence includes fields such as neural networks, fuzzy systems, evolutionary computing, and artificial life. The various types of fuzzy neural networks can also be considered. Neuro-fuzzy systems are tools for engineering technical processes, having an interest in engineering applications. Models are needed for training the process operators and for the development of soft sensors. Standard modeling approaches include two main streams: the first-principle (white-box) approach and the identification of a parameterized black-box model. The first-principle approach denotes models based on laws about physical and chemical phenomena, derived on the basis of the first principles whereas in the black-box models, the model structure is a priori selected. Based on experimental data, an identification algorithm is used for estimating the unknown parameters. The neuro-fuzzy methods are most useful in the identification of nonlinear and poorly known processes when measurement data giving examples of the process behavior are available. The fuzzy features in the approach also make it useful when knowledge is available in the form of human experimental knowledge. The integration of fuzzy methods with optimization, systems identification, and parameter estimation techniques offers robust and efficient tools for the modeling of real-world industrial processes. This chapter illustrates a unified representation including logic processors (LP), and multilayer perceptron (MLP) neural networks. It discusses gradient-based parameter estimation techniques, learning-automata-based techniques, and identification of a nonlinear plant.

A new approach based on a genealogical decision tree is suggested for solving an open-loop tracking problem. The algorithm associates Gaussian distributions to both the norms of the control actions and the tracking errors. It solves the optimization problem sequentially, using random resampling from a population of solutions. This stochastic search model can be interpreted as a simple genetic particle evolution model with a natural birth and death interpretation. It converges in probability. Two numerical examples, dealing with rapid thermal processing and robotics, illustrate the feasibility and the performance of this control algorithm.