Extending a Hybrid CBR-ANN Model by Modeling Predictive Attributes Using Fuzzy Sets

The information processing capability of artificial neural networks (ANNs) is closely related to its architecture and weights. The paper describes a new evolutionary system for evolving artificial feedforward neural networks, which is based on the particle swarm optimisation (PSO) algorithm. Both the architecture and the weights of ANNs are adaptively adjusted according to the quality of the neural network. This process is repeated until the best ANN is accepted or the maximum number of generations has been reached. A strategy of evolving added nodes and a partial training algorithm are used to maintain a close behavioural link between the parents and their offspring. This system has been tested on two real problems in the medical domain. The results show that ANNs evolved by PSONN have good accuracy and generalisation ability.

The considerable improvement of technology produced for various applications has resulted in a growth in data sizes, such as healthcare data, which is renowned for having a large number of variables and data samples. Artificial neural networks (ANN) have demonstrated adaptability and effectiveness in classification, regression, and function approximation tasks. ANN is used extensively in function approximation, prediction, and classification. Irrespective of the task, ANN learns from the data by adjusting the edge weights to minimize the error between the actual and predicted values. Back Propagation is the most frequent learning technique that is used to learn the weights of ANN. However, this approach is prone to the problem of sluggish convergence, which is especially problematic in the case of Big Data. In this paper, we propose a Distributed Genetic Algorithm based ANN Learning Algorithm for addressing challenges associated with ANN learning for Big data. Genetic Algorithm is one of the well-utilized bio-inspired combinatorial optimization methods. Also, it is possible to parallelize it at multiple stages, and this may be done in an extremely effective manner for the distributed learning process. The proposed model is tested with various datasets to evaluate its realizability and efficiency. The results obtained from the experiments show that after a specific volume of data, the proposed learning method outperformed the traditional methods in terms of convergence time and accuracy. The proposed model outperformed the traditional model by almost 80% improvement in computational time.

Virtual-environment-based instrument development system (VEBIDS) is a concept previously put forward by the author to develop instruments in a virtual environment (VE) generated by computer, with the support of appropriate computer hardware, software, virtual reality (VR) peripherals in accordance with specific applications. This paper first presents the integrated model of VEBIDS, then discusses the application of case-based reasoning (CBR) in it. To make the system more efficient and flexible, artificial neural network (ANN) is also adopted into the system to complement with CBR. Experimental results show that the combination of CBR and ANN in VEBIDS is satisfactory. With the appearance of more economical and user-affordable high-performance computers and VR peripherals, VEBIDS can become a very practical and useful tool in instrument development.

An artificial neural network (ANN) representation method is presented and discussed in this paper. ANN representation of symbolic and numeric knowledge is realized by the improved backpropagation (BP) algorithm. The method of management and organization of ANN representation is presented also. The problem that numeric knowledge is difficult to be represented is effectively solved by the network. After learning, the threshold values and weights of ANN are ensured and the diagnostic knowledge is learned. The practical examples are used to check the diagnostic results. It is shown that the ANN representation method is effective.

This paper introduces the genetic algorithm (GA) into the artificial neural network (ANN) for the parametric modeling of ultra-wideband bandpass filters. With the optimization of thresholds and weights in ANN, GA speeds up the convergence of ANN training. The trained ANN, instead of the time-consuming full-wave simulation, is repeatedly called in the design process, leading to an efficient solution.

Fuzzy concepts are represented by fuzzy sets, so a fuzzy knowledge is defined to be a collection of fuzzy sets (concepts) which includes two constants, 1 (true) and 0 (false). Abstractly, a logical derivation is a finite series of logical operations acting on a given knowledge. In fuzzy world, logical operations are defined by mathematical operations. So fuzzy logical derivations are mathematical derivations, and vice versa In this paper, the closure of fuzzy reasoning is characterized by fuzzy rough sets, and by topologies (neighborhood systems). A new fuzzy concept F N is derivable from the old knowledge K iff the membership function of F N is continuous in K-topology, or equivalently, iff F N is a rough fuzzy set (concept) of I N D(K),or plainly, iff the membership function of F N is a “step” function in the sense that it takes a constant value in each equivalence class of I N D(K).A fuzzy set L partitions the universe into equivalence classes; each equivalence class consists of those elements which have the same degree of membership. The indiscernibility relation I N D(K)over K is the “intersection” of all the equivalence relations induced by the fuzzy sets in K. The membership function of a fuzzy set is a real valued function, so U can be given the minimal topology such that the membership function of each fuzzy set in K is a continuous function. Such topology is called K-topology.

The Influence of Training Data Selection on Performance of Neural Networks for Control of Non-Linear Systems *

The influence of training data selection on performance of neural networks for control of non-linear systems

The fault location is an important issue to be studied in the context of smart grids and microgrids. Thus, this article presents a methodology based on Artificial Neural Networks (ANNs) to locate areas under fault. To train and validate the ANNs, the superimposed components of the voltage and current measurements, acquired by a single meter at the substation, were used. The proposed strategy was tested utilizing the medium voltage distribution system benchmark from CIGRE, including the analysis of different ANN topologies. After several simulations via the PSCAD™/EMTDC™ software, the analysis demonstrated that the proposed methodology is promising and leads to a high level of accuracy in locating areas under fault.

To manage the inventory efficiently, it is necessary to have accurate forecasting. To extract and deploy the knowledge associated with forecasting attracts the attention of both academic and practitioners. Knowledge is regarded as a valuable asset for enterprises and it can be manipulated through intelligence techniques like Artificial Neural Networks (ANN). ANN has the special ability to learn facts about one knowledge domain by inputting data obtained from observations. This study focuses on exploring how ANN learns and analyzes different types of ANN and ANN architectures used in the demand forecasting. The feasibility of the proposed approach to the demand forecasting issue is demonstrated with numeric data. The significance of this study is to adopt ANN as a knowledge discovery system thereby enhancing the inventory management.

AFTER MORE THAN TWO DECADES OF NEGLECT, artificial neural networks (ANNs) are emerging as a strong contender in artificial intelligence. However, researchers whose leanings are more toward expert systems or other types of symbolic formalisms for knowledge representations warn against overly enthusiastic claims depicting ANNs as replicating the process of human thinking. While some of these objections are justified, the technology warrants a closer look by designers of educational software. Though neural networks are usually considered as a part of artificial intelligence, they are very different from the AI formalisms to which we’re accustomed. Expert systems use if-then rules to arrive at an answer. These rules are formulated by a knowledge engineer who works closely with a content expert to derive a model of the problem space. Neural networks, however, are fed sample cases (called “training sets≓), learn how to recognize patterns in the sample data, and then use this experience in handling new cases. Because neural nets are trained rather than programmed, the only overhead — other than the initial design — is in collecting and formatting the data sets (Carlson, 1990b, p. 50). This paper first describes the essentials of how an ANN learns, using a classic application in pattern recognition to demonstrate an operating ANN. Of the several “cognitive≓ roles ANNs are suitable for, two seem especially appropriate for computer-mediated education: (1) intelligent filters and (2) spreading, associative memories. To illustrate each, I describe prototypes I’m working on at the Air Force Armstrong Laboratory.

There are several kinds of fuzzy set extensions in the fuzzy set theory. Among them, this paper is concerned with interval-valued fuzzy sets, intuitionistic fuzzy sets, and bipolar-valued fuzzy sets. In interval-valued fuzzy sets, membership degrees are represented by an interval value that reflects the uncertainty in assigning membership degrees. In intuitionistic fuzzy sets, membership degrees are described with a pair of a membership degree and a nonmembership degree. In bipolar-valued fuzzy sets, membership degrees are specified by the satisfaction degrees to a constraint and its counter-constraint. This paper investigates the similarities and differences among these fuzzy set representations.

This paper investigates the seismic performance of four-limb concrete-filled steel tubular (CFST) lattice columns with different slenderness ratios (10.8, 10.8, 18.4, and 27.9) and axial load ratios (0.2, 0.3, 0.2, and 0.2), conducting horizontal low-cycle reciprocating load test. Based on the test results, an artificial neural network (ANN) is used to improve the prediction of the seismic performance of four-limb CFST latticed columns, considering ANN's defects of low accuracy prediction and the poor fitting for the load, unload and extreme point of hysteresis curves, the sparrow search optimization algorithm (SSA) is adopted to optimize the ANN's weights and thresholds. Quantum computations are proposed to improve the SSA's iteration convergence and avoidance of local optima, and its convergence curve is compared to circle chaotic sparrow search algorithm and tent chaos sparrow search algorithm (Tent-CSSA). The damage variables are calculated and compared with the predicted results from the SSA, the sparrow search algorithm based on quantum computations and multi-strategy enhancement (QMESSA) and the test results based on the energy damage model and Park's model. The results demonstrate that the load-displacement hysteresis curves of Specimen 1 and Specimen 2 are bow-shaped, which show a strong plastic capacity. The hysteresis curve of Specimen 3 appears in an inverse S-shape, which was due to the slip caused by the low bond strength between concrete and steel pipe. The hysteresis curve of Specimen 4 is pike-shaped, which has a high shear span ratio and obvious bending performance. QMESSA effectively optimizes the weights and thresholds of the ANN, and its predicted damage variables are consistent with those of the conventional damage model. This indicates that QMESSA can effectively predict the load-displacement hysteresis curves of four-limb CFST lattice columns under low-cycle reciprocating loading.

The basic notions in rough set theory are lower and upper approximation operators defined by a fixed binary relation. This paper proposes an intuitionistic fuzzy rough set (IFRS) model which is a combination of intuitionistic fuzzy set (IFS) and rough set. We approximate an IFS by using soft binary relations instead of fixed binary relations. By using this technique, we get two pairs of intuitionistic fuzzy (IF) soft sets, called the upper approximation and lower approximation with respect to foresets and aftersets. Properties of newly defined rough set model (IFRS) are studied. Similarity relations between IFS with respect to this rough set model (IFRS) are also studied. Finally, an algorithm is constructed depending on these approximations of IFSs and score function for decision-making problems, although a method of decision-making algorithm has been introduced for fuzzy sets already. But, this new IFRS model is more accurate to solve the problem because IFS has degree of nonmembership and degree of hesitant.

Influence of random topology in artificial neural networks: A survey