George Bafas

Fuzzy model predictive control of non-linear processes using genetic algorithms

Abstract This paper introduces a new fuzzy control technique, which belongs to the popular family of control algorithms, called Model Predictive Controllers. The method is based on a dynamic fuzzy model of the process to be controlled, which is used for predicting the future behavior of the output variables. A non-linear optimization problem is then formulated, which minimizes the difference between the model predictions and the desired trajectory over the prediction horizon and the control energy over a shorter control horizon. The problem is solved on line using a specially designed genetic algorithm, which has a number of advantages over conventional non-linear optimization techniques. The method can be used with any type of fuzzy model and is particularly useful when a direct fuzzy controller cannot be designed due to the complexity of the process and the difficulty in developing fuzzy control rules. The method is illustrated via the application to a non-linear single-input single-output reactor, where a Takagi–Sugeno model serves as a predictor of the process future behavior.

A hierarchical fuzzy-clustering approach to fuzzy modeling

Abstract This paper introduces a new method for fuzzy modeling based on a hierarchical fuzzy-clustering scheme. The method consists of a sequence of steps aiming towards developing a Takagi–Sugeno (TS) fuzzy model of optimal structure, where the fuzzy sets in the premise part are of Gaussian type. Starting from an initial ordinary fuzzy partition of the input space, the algorithm performs a nearest-neighbor search and groups the original input training data into a number of clusters. The centers of these clusters are further processed using an optimal fuzzy clustering technique, which is based on the weighted fuzzy c -means algorithm. The resulted optimal fuzzy partition defines the number of fuzzy rules and provides an initial estimation for the system parameters, which in a next step are fine tuned using the well-known gradient-descend algorithm. The proposed method is successfully applied to three test examples, where the produced fuzzy models prove to be very accurate, as well as compact in size.

A new algorithm for developing dynamic radial basis function neural network models based on genetic algorithms

Abstract A new method for extracting valuable process information from input–output data is presented in this paper. The proposed methodology produces dynamical radial basis function (RBF) neural network models based on a specially designed genetic algorithm (GA), which is used to auto-configure the structure of the networks and obtain the model parameters. The new RBF network training technique formulates a complete optimization problem, which includes the network structure into the set of free variables that are used to minimize the prediction error. This is a different approach compared with the local search methods employed by other structure selection mechanisms, which are often trapped to local minima. Another advantage of the proposed method is that only one run of the algorithm is required to obtain the optimal network structure, in contrast to the standard RBF training techniques, where the produced model is selected by trial and error. The effectiveness of the method is illustrated through the development of dynamical models for two sets of data: simulated data from a Continuous Stirred Tank Reactor (CSTR) and true data collected from a Kamyr digester, which is a rather complicated reactor used in the pulp and paper industry.

A new algorithm for online structure and parameter adaptation of RBF networks

This paper deals with the problem of online adaptation of radial basis function (RBF) neural networks. A new adaptive training method is presented, which is able to modify both the structure of the network (the number of nodes in the hidden layer) and the output weights, as the algorithm proceeds. These adaptation capabilities make the algorithm suitable for modeling dynamical time varying systems, where not only the dynamics but also the operating region changes with time. Therefore, the important issue of extrapolation is faced successfully, but at the same time the algorithm takes care of the size of the network, by deleting the hidden node centers that remain inactive for a long time. The selection of the network centers is based on a fuzzy partition of the input space, which defines a number of fuzzy subspaces. The algorithm considers the centers of the fuzzy subspaces as candidates for becoming hidden node centers and makes the selections, so that at least one center is close enough to each input example. The proposed technique is illustrated through the application to time varying dynamical systems and is compared to other adaptive training methods.

A cellular automata model for forest fire spread prediction: The case of the wildfire that swept through Spetses Island in 1990

We present and illustrate the simulation results of a cellular automata model describing the dynamics of a forest fire spread on a mountainous landscape taking into account factors such as the type and density of vegetation, the wind speed and direction and the spotting phenomenon. The model is used to simulate the wildfire that broke up on Spetses in August of 1990 and destroyed a major part of the Island’s forest. We used a black-box non-linear optimization approach to fine-tune some of the model’s parameters based on a geographical information system incorporating available data from the real forest fire. The comparison between the simulation and the actual-observed results showed that the proposed model predicts in a quite adequate manner the evolution characteristics in space and time of the real incident and as such could be potentially used to develop a fire risk-management tool for heterogeneous landscapes.

A fast training algorithm for RBF networks based on subtractive clustering

Abstract A new algorithm for training radial basis function neural networks is presented in this paper. The algorithm, which is based on the subtractive clustering technique, has a number of advantages compared to the traditional learning algorithms, including faster training times and more accurate predictions. Due to these advantages the method proves suitable for developing models for complex nonlinear systems.

Wildland fire spread modelling using cellular automata: evolution in large-scale spatially heterogeneous environments under fire suppression tactics

We show how microscopic modelling techniques such as Cellular Automata linked with detailed geographical information systems (GIS) and meteorological data can be used to efficiently predict the evolution of fire fronts on mountainous and heterogeneous wild forest landscapes. In particular, we present a lattice-based dynamic model that includes various factors, ranging from landscape and earth statistics, attributes of vegetation and wind field data to the humidity of the fuel and the spotting transfer mechanism. We also attempt to model specific fire suppression tactics based on air tanker attacks utilising technical specifications as well as operational capabilities of the aircrafts. We use the detailed model to approximate the dynamics of a large-scale fire that broke out in a region on the west flank of the Greek National Park of Parnitha Mountain in June of 2007. The comparison between the simulation and the actual results showed that the proposed model predicts the fire-spread characteristics in an adequate manner. Finally, we discuss how such a detailed model can be exploited in order to design and develop, in a systematic way, fire risk management policies.

Optimal energy management in pulp and paper mills

Abstract In this paper, we examine the utilization of mathematical programming tools for optimum energy management of the power plant in pulp and paper mills. The objective is the fulfillment of the total plant requirements in energy and steam with the minimum possible cost. The proposed methodology is based on the development of a detailed model of the power plant using mass and energy balances and a mathematical formulation of the electrical purchase contract, which can be translated into a rigorous mixed integer linear programming optimization problem. The results show that the method can be a very useful tool for the reduction of production cost due to minimization of the fuel and electricity costs.

A simple algorithm for training fuzzy systems using input-output data

This paper proposes a simple algorithm for training fuzzy systems from numerical data. The main advantage of the method is the lack of complicated iterative mechanisms and therefore, its implementation is carried out easily. The suggested algorithm employs a fuzzy model with simplified rules, assuming a fuzzy partition of the input space into fuzzy subspaces. The output is inferred by expanding the model into fuzzy basis functions (FBFs), where each FBF corresponds to a certain fuzzy subspace. The number of rules and the respective premise parts (fuzzy subspaces) are determined using the nearest neighbor approach. Then, the optimal consequent parameters are obtained by the least-squares method. Finally, simulations show the validity of the method.

Design of a model identification fuzzy adaptive controller and stability analysis of nonlinear processes

This paper deals with the design of a model identification fuzzy adaptive controller with real-time scaling factors adjustment and the stability analysis of nonlinear distributed parameter systems. The solution branch of such systems frequently contains limit points (or turning points) which represent the boundary between stability and instability of the system. Hence, stability analysis is required for the determination of the stable and unstable operating regions. The performance of the proposed fuzzy self-tuning controller is compared to an equivalent conventional adaptive controller, over a wide range of step disturbances and operating regions. The proposed fuzzy adaptive scheme in comparison with the conventional adaptive scheme exhibits a much robust response, shorter settling times, overshooting less the controlled variable and smaller IAE of the manipulated variable for the entire range of step disturbances.