35441363
Consistency checking for fuzzy expert systems
Abstract Inconsistency frequently exists in a rule-based expert system. Detecting the existence of inconsistency in a fuzzy rule-based environment is difficult and may be different from that of traditional rule-based systems. An affinity measure, which is based on the similarity measure, is introduced to determine the likeness of two fuzzy terms. By using the affinity measure, the techniques for consistency checking in a non-fuzzy environment can be easily applied to a fuzzy environment. A consistency checker (CCFE) is implemented to detect possible inconsistency in a mixed fuzzy and non-fuzzy environment.
18388949
A probabilistic cooperative-competitive hierarchical model for global optimization
Abstract Stochastic searching methods have been applied widely to areas such as continuous and combinatorial optimization problems in a number of disciplines. Many existing methods solve these problems by navigating on the surface of the possibly rugged landscape. This kind of navigation is not very effective because the property of the landscape at different resolutions can be very different. Time spent at the beginning of the search on the detailed part of the landscape is often useless. Appropriate searching strategies should be adopted at different resolutions. In this paper, we propose a new probabilistic searching model for global optimization. The main contributions of the model are (1) to provide a basis for resolution control and smoothing of search space and (2) to introduce continuous memory into stochastic search. The basis of resolution control is achieved by dividing the search space into a finite number of n -dimensional partitions structurally. The number of partitions governs the resolution of the search space. The more the partitions, the finer is the search space and the more detailed and rugged is the landscape. The benefits are twofold. First, the rugged landscape problem can be smoothed, because the ruggedness is a matter of the number of partitions. Hence, the difficulty in search due to the ruggedness of the landscape can be controlled. Second, it provides a basis to implement algorithms that may change the ‘view’ of the landscape during the search process because we can dynamically divide the search space accordingly. Another important feature that we use is continuous memory . Throughout the search process, searching experience is continuously accumulated in order to shape the global picture of the search space guiding the future searching direction. We present results on the algorithm performance in handling numerical function optimization. The empirical results show that our new model is comparable to, and in many cases performs better than, that of the other advanced methods in terms of solution quality and computation required.
17795196
Scalable model-based clustering by working on data summaries
The scalability problem in data mining involves the development of methods for handling large databases with limited computational resources. We present a two-phase scalable model-based clustering framework: first, a large data set is summed up into subclusters; Then, clusters are directly generated from the summary statistics of subclusters by a specifically designed expectation-maximization (EM) algorithm. Taking example for Gaussian mixture models, we establish a provably convergent EM algorithm, EMADS, which embodies cardinality, mean, and covariance information of each subcluster explicitly. Combining with different data summarization procedures, EMADS is used to construct two clustering systems: gEMADS and bEMADS. The experimental results demonstrate that they run several orders of magnitude faster than the classic EM algorithm with little loss of accuracy. They generate significantly better results than other model-based clustering systems using similar computational resources.
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.
18293423
Using Evolutionary Programming and Minimum Description Length Principle for Data Mining of Bayesian Networks
We have developed a new approach to learning Bayesian network structures based on the minimum description length (MDL) principle and evolutionary programming. It employs a MDL metric, which is founded on information theory, and integrates a knowledge-guided genetic operator for the optimization in the search process.
6017611
Balancing Samples' Contributions on GA Learning
A main branch in Evolutionary Computation is learning a system directly from input/output samples without investigating thternal behaviors of the system. Input/output samples captured from a real system are usually incomplete, biased and noisy. In order to evolve a precise system, the sample set should include a complete set of samples. Thus, a large number of samples should be used. Fitness functions being used in Evohitionary Algorithms usually based on the matched ratio of samples. Unfortunately, some of these samples may be exactly or semantically duplicated. These duplicated samples cannot be identified simply because we do not know the internal behavior of the system being evolved. This paper proposes a method to overcome this problem by using a dynamic fitness function that incorporates the contribution of each sample in the evolutionary process. Experiments on evolving Finite State Machines with Genetic Algorithms are presented to demonstrate the effect on improving the successful rate and convergent speed of the proposed method.
320235
Handling signature purposes in workflow systems
Abstract In paper-based workflow systems, signatures of individuals or groups of people have been used extensively for different purposes. Currently there are numerous studies on computerizing workflow systems. Also there are studies on implementing digital signatures in electronic media. But the diversified purposes of a signature in workflow makes a straightforward implementation of digital signature schemes inadequate. There are few studies on the implication of different signature purposes on electronic workflow systems. The purposes of signature are closely associated with the two modes of decision making, namely single and group. These two modes of decision making in turn lead to single and group signatures. This paper reports our comprehensive studies on signature purposes. These include the analysis of common signature purposes in workflow, classification of these purposes, classification of modes of decision making associated with these signature purposes, signing and validation requirements for handling these signature purposes, and finally an architecture to be incorporated in workflow engines for handling these signature purposes. In summary, this paper addresses a commonly neglected problem in information systems research: management control by signature in workflow.
28941391
Optimum nested Miller compensation for low-voltage low-power CMOS amplifier design
An optimum structure of nested Miller compensation (NMC) is developed. The developed structure using a feedforward transconductance stage and null resistor on NMC, called NMCFNR, solves the bandwidth reduction and poor transient response problems of low-voltage low-power NMC amplifiers. For comparison, amplifiers compensated by NMC and NMCFNR have been fabricated. Experimental results show that NMCFNR improves the gain-bandwidth product, slew rate and settling time by more than three times with 8/spl deg/ increase on the phase margin and no increase of the power consumption. Moreover, it improves the negative power supply rejection ratio by at least 54 dB. The die area of the NMCFNR amplifier is about half of that of the NMC amplifier.
5284748
A novel frequency compensation technique for low-voltage low-dropout regulator
A novel frequency compensation technique for low-voltage low-dropout regulator (LDR) is presented. The proposed technique, called pole-control frequency compensation (PCFC), provides faster loop response and does not require a large filtering capacitor. Both theoretical analysis and simulation show that the stability of the LDR using PCFC is independent of the load current, temperature and equivalent series resistance of the filtering capacitor. LDRs are well suited to portable equipment such as cellular phones, pagers and personal digital assistants.
39521573
An inversion algorithm for loss networks with state-dependent rates
We extend our recently developed algorithm for computing (exact) steady-state blocking probabilities for each class in product-form loss networks to cover general state-dependent arrival and service rates. This generalization allows us to consider, for the first time, a wide variety of buffered and unbuffered resource-sharing models with non-Poisson traffic as may arise with overflows in the context of alternative routing. As before, we consider non-complete-sharing policies involving upper-limit and guaranteed-minimum bounds for the different classes, but here we consider both bounds simultaneously. Major features of the algorithm are: dimension reduction by conditional decomposition based on special structure, an effective scaling algorithm to control errors in the inversion, the efficient treatments of multiple classes with identical parameters and the truncation of large sums.
14053268
Data Synchronization Methods Based on ShuffleNet and Hypercube for Networked Information Systems
In contrast to a typical single source of data updates in Internet applications, data files in a networked information system are often distributed, replicated, accessed and updated by multiple nodes. Due to concurrent updates, replicated data files must be synchronized. For certain applications, stringent concurrency control must be employed to ensure data integrity, while for other applications, periodic data synchronization may enable very efficient data sharing. For the latter applications, this paper devises the ShuffleNet and hypercube schemes for data synchronization in such networked information systems. Their performance in terms of update delay, processing complexity, failure tolerance and growth complexity is examined. Our results reveal that the ShuffleNet and hypercube scheme provide identical maximum update delay and similar processing complexity. However, as the number of nodes in the system changes (e.g., due to failure or temporary out of service for maintenance), the hypercube scheme maintains all existing synchronization sessions and greatly simplifies system administration overhead such as moving files from node to node for the purpose of data synchronization. The ShuffleNet scheme does provide a higher degree of failure tolerance for global data files, but the hypercube scheme provides more than adequate failure tolerance. Lastly, a generalization of the hypercube scheme, based on the ideas of shift registers, is also proposed for systems where the number of nodes is a perfect power of 2.