Harold W. Lewis

Remarks on “Measuring Ambiguity in the Evidence Theory”

In a recent paper, a functional AM (ambiguity measure) is introduced and an attempt is made to show that this functional qualifies as a measure of total aggregated uncertainty in the Dempster-Shafer theory. We show that this attempt fails due to a particular error in the proof of one of the principal theorems in the paper. Some additional remarks are made regarding recent research pertaining to the subject of the discussed paper.

Concepts and fuzzy sets: Misunderstandings, misconceptions, and oversights

The psychology of concepts has been undergoing significant changes since the early 1970s, when the classical view of concepts was seriously challenged by convincing experimental evidence that conceptual categories never have sharp boundaries. Some researchers recognized already in the early 1970s that fuzzy set theory and fuzzy logic were potentially suitable for modeling of concepts and obtained encouraging results. This positive attitude abruptly changed in the early 1980s, and since that time fuzzy set theory and fuzzy logic have been portrayed as problematic and unsuitable for representing and dealing with concepts. Our aim in this paper is to identify some of the most notorious claims regarding fuzzy set theory and fuzzy logic that have propagated through the literature on psychology of concepts and to show that they are, by and large, false. We trace the origin and propagation of these claims within the literature in this area. It is shown in detail that these claims are consistently erroneous and that they are based on various misunderstandings, misconceptions, and oversights. The ultimate purpose of this paper is to document these various erroneous claims.

Optimization of the stochastic dynamic production cycling problem by a genetic algorithm

A production system to produce products of multiple items by several machines to meet time-varying stochastic demand is considered. The planning horizon is finite and divided into discrete periods. The demand in each period is mutually independent random variable whose probability distribution is known. Each machine can process at most one item in each period. Setup cost and setup time are incurred only when a machine changes from production of one item to another. Though this kind of problem can be formulated as a Markov decision model, it requires prohibitively long time to obtain a solution. Therefore, an eclectic model is proposed, where items are treated as variables to be determined at the beginning of the planning horizon and production quantities are determined as a policy. The objective function to be minimized is the expectation of the sum of production costs, inventory-holding costs, shortage costs and setup costs. A solution procedure consisting of a genetic algorithm and dynamic programming is proposed to obtain a near-optimal solution for the eclectic model. Three kinds of computational experiments are provided. First, we investigate preliminarily the difference between the optimal value for our eclectic model and the optimal value for the pure Markov decision model in which both items and production quantities are determined as a policy. It has been seen that the difference of the optimal values for the two models is small and the proposed eclectic model is effective. Secondly, we evaluate preliminarily the performance of the genetic algorithm itself for a deterministic model with a single machine that is a special case of the eclectic model. We have found that the genetic algorithm is so effective that we can apply it to the eclectic model. Thirdly, we provide main computational experiments to evaluate the performance of the proposed solution procedure consisting of the genetic algorithm and dynamic programming for the eclectic model. It has been found that good solutions can be obtained efficiently by the proposed solution procedure.

Intelligent hybrid load forecasting system for an electric power company

The paper presents a system for day-ahead load forecasting as originally proposed to a regional electric power company. The company provided funding for developing most parts of this software. The system is based on a hybrid approach to intelligent systems design combining a fuzzy heuristic approach based on the knowledge of human experts in load forecasting with a data-driven neural network-based component. To make the system truly useful, considerable emphasis was placed on the user interface including a highly developed explanation module.

A systems approach for scheduling aircraft landings in JFK airport

The aircraft landings scheduling problem at an airport has become very challenging due to the increase of air traffic. Traditionally, this problem has been widely studied by formulating it as an optimization model solved by various operation research approaches. However, these approaches are not able to capture the dynamic nature of the aircraft landing scheduling problem appropriately and handle uncertainty easily. A systems approach provides an alternative to solve such a problem from a systematic perspective. In this regard, the concept of general systems problem solving (GSPS) was first introduced in 1970s, and yet the power of the GSPS methodology is not fully discovered as it had only been applied to few domains. In this paper, a new general systems problem solving framework integrating computational intelligence techniques (GSPS-CI) is introduced. The two main functions of the framework are: (1) adaptive network based fuzzy inference system (ANFIS) to predict flight delays, and (2) fuzzy decision making procedure to schedule aircraft landings. The effectiveness of the GSPS-CI framework is tested on the JFK airport in USA, one of the most complex real-life systems.

Prediction of Mortality and Survival of Patients After Cardiac Surgery Using Fuzzy EuroSCORE System and Reliability Analysis

Abstract Cardiac surgery is an important medical treatment for coronary vessel patients. Different models have been introduced to determine the risk factors related to side effects of this operation. The goal of this research is to study EuroSCORE (European System for Cardiac Operative Risk Evaluation) as a useful method for predicting the risk of mortality after cardiac surgery, and to introduce a new way of inference, called Fuzzy EuroSCORE. In addition, a systems reliability analysis will be used to calculate the survival possibility of patients after a certain time period after cardiac surgery. To model and simulate the suggested system, eight important parameters of EuroSCORE table are chosen using experts knowledge and a new method is applied based on a fuzzy inference system. To calculate the risk of mortality after cardiac surgery, the patients are categorized into 3 different groups of low risk, medium risk, and high risk. The range of the mortality risk is determined by appropriate medical data in the fuzzy EuroSCORE system. Additionally, a defect density function for the cardiovascular problem is suggested using the systems reliability analysis. Finally, the prospect of patients survival after a certain time period after cardiac surgery is predicted.

Simulation of cellular compaction and internalization in mammalian embryo development—II. Models for spherical embryos

A model based upon minimization of surface energy as an explanation for the phenomena of compaction and internalization of cells during mammalian embryo development is generalized for three-dimensional cells. It is shown that, for a spherical embryo, if cells are assumed to be polygonal cones in shape, the simulation of these phenomena for three-dimensional cells is equivalent to simulations of deformations of two-dimensional cells on the surface of a sphere. This equivalence is used to show that in the optimal compacted structure, with no internal cells, the cross-sections of cells in general are not regular polyhedra. Further, the internalization occurs when the number of cells exceeds a critical value which seems to depend on the relative sizes and biophysical properties of cells.

Erratum to “Concepts and fuzzy sets: Misunderstandings, misconceptions, and oversights” [IJA 15 (2009) 23--34]

We regret that, due to publishers oversight, the printed version of this article contains two errors: 1. Eq. (1) on page 29, the parentheses are misplaced; the correct formula is

Review of: “INTELLIGENT HYBRID SYSTEMS —0 FUZZY LOGIC, NEURAL NETWORKS, AND GENETIC ALGORITHMS”, edited by Da Ruan. Kluwer Academic Publishers, Boston, 1997. XIX + 354 pages.

A key concept in soft computing and computational intelligence is the assumption of a natural synergy when combining two or more of the component methodologies, particularly fuzzy, neural, and genetic methods. It is primarily in this context that this book uses the term, hybrid systems. As an edited volume with contributions from many of the leading researchers in the field, Intelligent Hybrid Systems places much of its emphasis on discussing the latest developments. However, there are also elements that make it possible to use this as a textbook for graduate level, or as an introductory source for engineers. First, Hideyuki Takagi does a remarkable job of presenting the basic concepts of fuzzy, neural, and genetic methods as well as some common ways of combining them in just thirty pages of the first chapter. Second, Da Ruan skillfully categorizes the several advanced topics discussed by various authors in the other thirteen chapters, and presents them as a fairly well integrated whole. The three parts of the book are: