18463934
Design of a 1.5-V high-order curvature-compensated CMOS bandgap reference
A low-voltage low-noise high-order curvature-compensated CMOS bandgap reference is proposed in this paper. Two resistor strings are added to the bandgap circuit to reduce the supply voltage. In addition, a temperature-dependent resistor ratio generated by a high-resistive poly resistor is used to provide the high-order compensation. The proposed bandgap voltage reference can operate down to a 1.5V supply (where V/sub th/ = 0.9V at 0/spl deg/C) with a temperature coefficient of less than 15.2ppm//spl deg/C and an average line regulation of 5.5mV/V.
15996223
A low-voltage CMOS low-dropout regulator with enhanced loop response
A 1.5-V 100-mA CMOS low-dropout regulator with a double pole-zero cancellation scheme and a linearly operated power PMOS transistor at dropout to enhance the loop-gain response, is presented. The circuit realization is well-studied and developed with respect to the loop-gain response, the transient response, the output noise and the output accuracy, as well as the standby power consumption. Implemented in a 0.6 /spl mu/m CMOS process, experimental results show that the regulator provides a full load transient response of less than 1-/spl mu/s settling time and less than 50-mV overshoots and undershoots. Moreover, it provides a ripple rejection of better than -26 dB and an output noise of 0.07 /spl mu/V/sqrt Hz at 100 kHz.
1295488
Medical data mining using evolutionary computation
In this paper, we introduce a system for discovering medical knowledge by learning Bayesian networks and rules. Evolutionary computation is used as the search algorithm. The Bayesian networks can provide an overall structure of the relationships among the attributes. The rules can capture detailed and interesting patterns in the database. The system is applied to real-life medical databases for limb fracture and scoliosis. The knowledge discovered provides insights to and allows better understanding of these two medical domains.
2011139
Discovering knowledge from noisy databases using genetic programming
In data mining, we emphasize the need for learning from huge, incomplete and imperfect data sets (Fayyad et al. 1996, Frawley et al. 1991, Piatetsky-Shapiro and Frawley, 1991). To handle noise in the problem domain, existing learning systems avoid overfitting the imperfect training examples by excluding insignificant patterns. The problem is that these systems use a limiting attribute-value language for representing the training examples and the induced knowledge. Moreover, some important patterns are ignored because they are statistically insignificant. In this paper, we present a framework that combines Genetic Programming (Koza 1992; 1994) and Inductive Logic Programming (Muggleton, 1992) to induce knowledge represented in various knowledge representation formalisms from noisy databases. The framework is based on a formalism of logic grammars and it can specify the search space declaratively. An implementation of the framework, LOGENPRO (The Logic grammar based GENetic PROgramming system), has been developed. The performance of LOGENPRO is evaluated on the chess endgame domain. We compare LOGENPRO with FOIL and other learning systems in detail and find its performance is significantly better than that of the others. This result indicates that the Darwinian principle of natural selection is a plausible noise handling method which can avoid overfitting and identify important patterns at the same time. Moreover, the system is applied to one real-life medical database. The knowledge discovered provides insights to and allows better understanding of the medical domains. Area: Data mining, Genetic Programming, Evolutionary Computation, Rule Learning
206639069
A Modified Edge Recombination Operator for the Travelling Salesman Problem
Edge recombination is a crossover operator developed to preserve edge information for the Travelling Salesman Problem. This paper describes a modified version of the operator which converges significantly faster for all the benchmark problems tested.
18141920
Learning functional dependency networks based on genetic programming
Bayesian Network (BN) is a powerful network model, which represents a set of variables in the domain and provides the probabilistic relationships among them. But BN can handle discrete values only; it cannot handle continuous, interval and ordinal ones, which must be converted to discrete values and the order information is lost. Thus, BN tends to have higher network complexity and lower understandability. In this paper, we present a novel dependency network which can handle discrete, continuous, interval and ordinal values through functions; it has lower network complexity and stronger expressive power; it can represent any kind of relationships; and it can incorporate a-priori knowledge though user-defined functions. We also propose a novel Genetic Programming (GP) to learn dependency networks. The novel GP does not use any knowledge-guided nor application-oriented operator, thus it is robust and easy to replicate. The experimental results demonstrate that the novel GP can successfully discover the target novel dependency networks, which have the highest accuracy and the lowest network complexity.
44655302
A generic concept-based object-oriented geographical information system
Unlike most of the current object-oriented geographical information systems (OOGISs) whose designs are based on the traditional spatial conceptual model emphasizing the processing of geometric features, the concept-based OOGIS proposed in this paper provides a spatial conceptual model which comprises rich spatial semantics fundamental to spatial analysis, and an object-oriented data model (OODM) which provides an appropriate and effective representation of the spatial conceptual model for efficient database management. By structuring the cognition of space through three interrelated hierarchies: namely the spatial conceptual hierarchy, the entity hierarchy and the feature hierarchy, the generic concept-based OOGIS renders an appropriate framework for the scientific investigation of space and the design of an efficient object-oriented database management system. In addition to its generic nature, the proposed OOGIS is in line withour commonsense conceptualization of space. Furthermore, it can entertain mul...