Meta-heuristics are heuristic procedures used to tune, control, guide, allocate computational resources or reason about object-level problem solvers in order to improve their quality, performance, or efficiency. Offline meta-heuristics define the best structural and/or parametric configurations for the object-level model, while on-line heuristics generate run-time corrections for the behavior of the same object-level solvers. Soft Computing is a framework in which we encode domain knowledge to develop such meta-heuristics. We explore the use of meta-heuristics in three application areas: a) control; b) optimization; and c) classification. In the context of control problems, we describe the use of evolutionary algorithms to perform offline parametric tuning of fuzzy controllers, and the use of fuzzy supervisory controllers to perform on-line mode-selection and output interpolation. In the area of optimization, we illustrate the application of fuzzy controllers to manage the transition from exploration to exploitation of evolutionary algorithms that solve the optimization problem. In the context of discrete classification problems, we have leveraged evolutionary algorithms to tune knowledge-based classifiers and maximize their coverage and accuracy.
KEYWORDS: Data modeling, Principal component analysis, Sensors, Process modeling, Digital filtering, Feature selection, Computer simulations, Fuzzy logic, Fuzzy systems, Data acquisition
Hybrid soft computing models, based by neural, fuzzy and evolutionary computation technologies, have been applied to a large number of classification, prediction, and control problems. This paper focuses on one of such applications and presents a systematic process for building a predictive model to estimate time-to-breakage and provide a web break tendency indicator in the wet-end part of paper making machines. Through successive information refinement of information gleaned from sensor readings via data analysis, principal component analysis (PCA), adaptive neural fuzzy inference system (ANFIS), and trending analysis, a break tendency indicator was built. Output of this indicator is the break margin. The break margin is then interpreted using a stoplight metaphor. This interpretation provides a more gradual web break sensitivity indicator, since it uses more classes compared to a binary indicator. By generating an accurate web break tendency indicator with enough lead-time, we help in the overall control of the paper making cycle by minimizing down time and improving productivity.
KEYWORDS: Diagnostics, Fuzzy logic, Failure analysis, Systems modeling, Data modeling, Neural networks, Sensors, Cements, Control systems, Magnetic resonance imaging
We describe modeling techniques from the field of Soft Computing (SC), and we illustrate their use in solving diagnostics and prognostics problems. Soft Computing is an association of computing methodologies that includes as its principal members fuzzy, neural, evolutionary, and probabilistic computing. These methodologies enable us to deal with imprecise, uncertain data and incomplete domain knowledge typically encountered in real-world applications. We analyze five successful SC case studies of applications to equipment diagnostics, forecasting, and control, e.g., prediction of voltage breakdown in power distribution networks, prediction of paper web breakage in paper mills, raw mix proportioning control in cement plants, diagnostics of power generation faults, and classification of MRI signatures for incipient failure detection. We conclude by projecting future trends of SC technologies and their use in constructing hybrid SC systems.
Soft computing is a new field of computer sciences that deals with the integration of problem- solving technologies such as fuzzy logic, probabilistic reasoning, neural networks, and genetic algorithms. Each of these technologies provide us with complementary reasoning and searching methods to solve complex, real-world problems. We will analyze some of the most synergistic combinations of self computing technologies, with an emphasis on the development of smart algorithm-controllers, such as the use of FL to control GAs and NNs parameters. We will also discuss the application of GAs to evolve NNs or tune FL controllers; and the implementation of FL controllers as NNs tuned by backpropagation-type algorithms. We will conclude with a detailed description of a GA-tuned fuzzy controller to implement a train handling control.
We will provide a brief description of the field of approximate reasoning systems, with a particular emphasis on the development of fuzzy logic control (FLC). FLC technology has drastically reduced the development time and deployment cost for the synthesis of nonlinear controllers for dynamic systems. As a result we have experienced an increased number of FLC applications. In a recently published paper we have illustrated some of our efforts in FLC technology transfer, covering projects in turboshaft aircraft engine control, stream turbine startup, steam turbine cycling optimization, resonant converter power supply control, and data-induced modeling of the nonlinear relationship between process variable in a rolling mill stand. These applications will be illustrated in the oral presentation. In this paper, we will compare these applications in a cost/complexity framework, and examine the driving factors that led to the use of FLCs in each application. We will emphasize the role of fuzzy logic in developing supervisory controllers and in maintaining explicit the tradeoff criteria used to manage multiple control strategies. Finally, we will describe some of our FLC technology research efforts in automatic rule base tuning and generation, leading to a suite of programs for reinforcement learning, supervised learning, genetic algorithms, steepest descent algorithms, and rule clustering.
We view fuzzy logic control technology as a high level language in which we can efficiently define and synthesize non-linear controllers for a given process. We contrast fuzzy proportional integral (PI) controllers with conventional PI and 2D sliding mode controllers. Then we compare the development of fuzzy logic controllers (FLC) with that of knowledge-based system (KBS) applications. We decompose the comparison into reasoning tasks (representation, inference, and control) and application tasks (acquisition, development, validation, compilation and deployment). After reviewing the reasoning tasks, we focus on the compilation of fuzzy rule bases into fast access lookup tables. These tables can be used by a simplified run-time engine to determine the FLC's crisp output for a given input. Finally we illustrate the application of FLC technology in a hierarchical architecture to control a complex power plant for heavy vehicles.
Advanced real-time digital controls for complex plants or processes will use a model (an " Observer" ) which predicts the values for sensor readings expected from the actual plant these vote as alternate " sensors" if the real ones fail. We are exploring further use of the Observer for real-time embedded diagnostics based on high speed fuzzy logic chips just becoming available. We have established a Fuzzy Inferencing Test Bed for fuzzy logic applications. It uses a set of development tools which allow applications to be built and tested against simulated systems and then ported directly to a high speed fuzzy logic chip. With the Fuzzy Inferencing Test we investigate very high speed fuzzy logic to: isolate faults using static information and early fault information that evolves rapidly in time validate and smooth readings from redundant sensors and smoothly select alternate control modes in intelligent controllers. This paper reports our experience with fuzzy logic in these kinds of applications.
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