Automatically evolving rule induction algorithms with grammar-based genetic programming

In the last 30 years, research in the field of rule induction algorithms produced a large number of algorithms. However, these algorithms are usually obtained from the combination of a basic rule induction algorithm (typically following the sequential covering approach) with new evaluation functions, pruning methods and stopping criteria for refining or producing rules, generating many "new" and more sophisticated sequential covering algorithms. We cannot deny that these attempts to improve the basic sequential covering approach have succeeded. Hence, if manually changing these major components of rule induction algorithms can result in new, significantly better ones, why not to automate this process to make it more cost-effective? This is the core idea of this work: to automate the process of designing rule induction algorithms by means of grammar-based genetic programming. Grammar-based Genetic Programming (GGP) is a special type of evolutionary algorithm used to automatically evolve computer programs. The most interesting feature of this type of algorithm is that it incorporates a grammar into its search mechanism, which expresses prior knowledge about the problem being solved. Since we have a lot of previous knowledge about how humans design rule induction algorithms, this type of algorithm is intuitively a suitable tool to automatically evolve rule induction algorithms. The grammar given to the proposed GGP system includes knowledge about how humans- design rule induction algorithms, and also presents some new elements which could work in rule induction algorithms, but to the best of our knowledge were not previously tested. The GG P system aims to evolve rule induction algorithms under two different frameworks, as follows. In the first framework, the GGP is used to evolve robust rule induction algorithms, i.e., algorithms which were designed to be applied to virtually any classification data set, like a manually-designed rule induction algorithm. In the second framework, the GGP is applied to evolve rule induction algorithms tailored to a specific application XVI domain, i.e., rule induction algorithms tailored to a single data set. Note that the latter framework is hardly feasible on a hard scale in the case of conventional, manually-designed algorithms, since the number of classification data sets greatly outnumbers the number of rule induction algorithms designers. However, it is clearly feasible on a large scale when using the proposed system, which automates the process of rule induction algorithm design and implementation. Overall, extensive computational experiments with 20 VCI data sets and 5 bioinformatics data sets showed that effective rule induction algorithms can be automatically generated using the GGP in both frameworks. Moreover, the automatically evolved rule induction algorithms were shown to be competitive with (and overall slightly better than) four well-known manually designed rule induction algorithms when comparing their predictive accuracies. The proposed GGP system was also compared to a grammar-based hillclimbing system, and experimental results showed that the GGP system is a more effective method to evolve rule induction algorithms than the grammar-based hillclimbing method. At last, a multi-objective version of the GGP (based on the concept of Pareto dominance) was also proposed, and experiments were performed to evolve robust rule induction algorithms which generate both accurate and simple models. The results showed that in most of the cases the GGP system can produce rule induction algorithms which are competitive in predictive accuracy to wellknown human-designed rule induction algorithms, but generate simpler classification modes - i.e., smaller rule sets, intuitively easier to be interpreted by the user

Performance of Induction Machines

Induction machines are one of the most important technical applications for both the industrial world and private use. Since their invention (achievements of Galileo Ferraris, Nikola Tesla, and Michal Doliwo-Dobrowolski), they have been widely used in different electrical drives and as generators, thanks to their features such as reliability, durability, low price, high efficiency, and resistance to failure. The methods for designing and using induction machines are similar to the methods used in other electric machines but have their own specificity. Many issues discussed here are based on the fundamental achievements of authors such as Nasar, Boldea, Yamamura, Tegopoulos, and Kriezis, who laid the foundations for the development of induction machines, which are still relevant today. The control algorithms are based on the achievements of Blaschke (field vector-oriented control) and Depenbrock or Takahashi (direct torque control), who created standards for the control of induction machines. Today’s induction machines must meet very stringent requirements of reliability, high efficiency, and performance. Thanks to the application of highly efficient numerical algorithms, it is possible to design induction machines faster and at a lower cost. At the same time, progress in materials science and technology enables the development of new machine topologies. The main objective of this book is to contribute to the development of induction machines in all areas of their applications

Electromechanical actuation for thrust vector control applications

At present, actuation systems for the Thrust Vector Control (TVC) for launch vehicles are hydraulic systems. The Advanced Launch System (ALS), a joint initiative between NASA and the Air Force, is a launch vehicle that is designed to be cost effective, highly reliable and operationally efficient with a goal of reducing the cost per pound to orbit. As part of this initiative, an electromechanical actuation system is being developed as an attractive alternative to the hydraulic systems used today. NASA-Lewis is developing and demonstrating an Induction Motor Controller Actuation System with a 40 hp peak rating. The controller will integrate 20 kHz resonant link Power Management and Distribution (PMAD) technology and Pulse Population Modulation (PPM) techniques to implement Field Oriented Vector Control (FOVC) of a new advanced induction motor. Through PPM, multiphase variable frequency, variable voltage waveforms can be synthesized from the 20 kHz source. FOVC shows that varying both the voltage and frequency and their ratio (V/F), permits independent control of both torque and speed while operating at maximum efficiency at any point on the torque-speed curve. The driver and the FOVC will be microprocessor controlled. For increased system reliability, a Built-in Test (BITE) capability will be included. This involves introducing testability into the design of a system such that testing is calibrated and exercised during the design, manufacturing, maintenance and prelaunch activities. An actuator will be integrated with the motor controller for performance testing of the EMA TVC system. The design and fabrication of the motor controller is being done by General Dynamics Space Systems Division. The University of Wisconsin-Madison will assist in the design of the advanced induction motor and in the implementation of the FOVC theory. A 75 hp electronically controlled dynamometer will be used to test the motor controller in all four quadrants of operation using flight type control algorithms. Integrated testing of the controller and actuator will be conducted at a facility yet to be named. The EMA system described above is discussed in detail

Application of Fuzzy control algorithms for electric vehicle antilock braking/traction control systems

Abstract—The application of fuzzy-based control strategies has recently gained enormous recognition as an approach for the rapid development of effective controllers for nonlinear time-variant systems. This paper describes the preliminary research and implementation of a fuzzy logic based controller to control the wheel slip for electric vehicle antilock braking systems (ABSs). As the dynamics of the braking systems are highly nonlinear and time variant, fuzzy control offers potential as an important tool for development of robust traction control. Simulation studies are employed to derive an initial rule base that is then tested on an experimental test facility representing the dynamics of a braking system. The test facility is composed of an induction machine load operating in the generating region. It is shown that the torque-slip characteristics of an induction motor provides a convenient platform for simulating a variety of tire/road - driving conditions, negating the initial requirement for skid-pan trials when developing algorithms. The fuzzy membership functions were subsequently refined by analysis of the data acquired from the test facility while simulating operation at a high coefficient of friction. The robustness of the fuzzy-logic slip regulator is further tested by applying the resulting controller over a wide range of operating conditions. The results indicate that ABS/traction control may substantially improve longitudinal performance and offer significant potential for optimal control of driven wheels, especially under icy conditions where classical ABS/traction control schemes are constrained to operate very conservatively

An experimental laboratory bench setup to study electric vehicle antilock braking / traction systems and their control

This paper describes the preliminary research and implementation of an experimental test bench set up for an electric vehicle antilock braking system (ABS)/traction control system (TCS) representing the dry, wet and icy road surfaces. A fuzzy logic based controller to control the wheel slip for electric vehicle antilock braking system is presented. The test facility comprised of an induction machine load operating in the generating region. The test facility was used to simulate a variety of tire/road μ-σ driving conditions, eliminating the initial requirement for skid-pan trials when developing algorithms. Simulation studies and results are provided

A methodology for the generation of efficient error detection mechanisms

A dependable software system must contain error detection mechanisms and error recovery mechanisms. Software components for the detection of errors are typically designed based on a system specification or the experience of software engineers, with their efficiency typically being measured using fault injection and metrics such as coverage and latency. In this paper, we introduce a methodology for the design of highly efficient error detection mechanisms. The proposed methodology combines fault injection analysis and data mining techniques in order to generate predicates for efficient error detection mechanisms. The results presented demonstrate the viability of the methodology as an approach for the development of efficient error detection mechanisms, as the predicates generated yield a true positive rate of almost 100% and a false positive rate very close to 0% for the detection of failure-inducing states. The main advantage of the proposed methodology over current state-of-the-art approaches is that efficient detectors are obtained by design, rather than by using specification-based detector design or the experience of software engineers

Niching genetic algorithms for optimization in electromagnetics. I. Fundamentals

Niching methods extend genetic algorithms and permit the investigation of multiple optimal solutions in the search space. In this paper, we review and discuss various strategies of niching for optimization in electromagnetics. Traditional mathematical problems and an electromagnetic benchmark are solved using niching genetic algorithms to show their interest in real world optimization