Changes under the hood - a new type of non-singleton fuzzy logic system

A major asset of fuzzy logic systems is dealing with uncertainties arising in their various applications, thus it is important to make them achieve this task as effectively and comprehensively as possible. While singleton fuzzy logic systems provide some capacity to deal with such uncertainty aspects, non-singleton fuzzy logic systems (NSFLSs) have further enhanced this capacity, particularly in handling input uncertainties. This paper proposes a novel approach to NSFLSs, which further develops this potential by changing the method of handling input fuzzy sets within the inference engine. While the standard approach is getting the maximum of the intersection between input’s and antecedent’s fuzzy sets (in the ”pre-filtering” stage), it is proposed to employ the centroid of the intersection as the basis of each rule’s firing degree. The motivation is to capture the interaction of input and antecedent fuzzy sets with high fidelity, thus making NSFLSs more sensitive to the input’s uncertainty information. The testbed is the common problem of Mackey-Glass time series prediction in the presence of input noise. Analyses of the results show that the new method outperforms the standard approach (by reducing the prediction error) and has potential for a more efficient uncertainty handling in NSFLS applications

Applications of Soft Computing in Mobile and Wireless Communications

Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications

Face Validation Method Alternatives for Shiphandling Fuzzy Logic Difficulty Model

The development of shiphandling difficulty model for ferry is based on the empirical experience through the Master of Ro-Ro ferries. The SHDMF is consisted from two parts which are the Analytic Hierarchy Process (AHP) and Fuzzy Inference System. Both parts had been validated through internal validation in the form of consistency test for the first part and robustness test for the second part. Further, the external/face validation is required to compare the proposed model with similar model through benchmarking approach. The benchmarking approaches are elaborated for the reliability, validity, possibility, efficiency and effectiveness. Through fuzzy group decision making method, the questionnaire survey is performed to verify the most appropriate approach based on the shiphandling simulator as the most preferred benchmarking tool by experts. Next, the proposed scenario is overviewed and discussed especially related to the advantages and drawbacks of shiphandling simulator. Keywords: shiphandling difficulty, fuzzy group decision making, internal validation Model pengukuran kesulitan pengendalian feri didasarkan pada pengalaman empiris melalui pernyataan nahkoda kapal feri Ro-Ro. SHDMF terdiri atas dua bagian, yaitu Analytic Hierarchy Process dan Fuzzy Inference System. Kedua bagian ini telah divalidasi melalui validasi internal dalam bentuk uji konsistensi untuk bagian pertama dan uji kehandalan untuk bagian kedua. Selanjutnya validasi atau wajah eksternal diperlukan untuk membandingkan model yang diusulkan dengan model yang diperoleh dari benchmarking. Pendekatan benchmarking dijabarkan untuk kehandalan, validitas, kemungkinan, efisiensi, dan efektivitas. Melalui metode fuzzy kelompok pembuatan keputusan, survei kuesioner dilakukan untuk memverifikasi pendekatan yang paling tepat dengan simulator pengendalian kapal sebagai alat yang paling disukai oleh para ahli untuk benchmarking. Selanjutnya skenario yang ditinjau-ulang dan dibahas terutama terkait dengan keuntungan dan kelemahan simulator pengendalian kapal. Kata

A simple hybrid algorithm for improving team sport AI

In the very popular genre of team sports games defeating the opposing AI is the main focus of the gameplay experience. However the overall quality of these games is significantly damaged because, in a lot of cases, the opposition is prone to mistakes or vulnerable to exploitation. This paper introduces an AI system which overcomes this failing through the addition of simple adaptive learning and prediction algorithms to a basic ice hockey defence. The paper shows that improvements can be made to the gameplay experience without overly increasing the implementation complexity of the system or negatively affecting its performance. The created defensive system detects patterns in the offensive tactics used against it and changes elements of its reaction accordingly; effectively adapting to attempted exploitation of repeated tactics. This is achieved using a fuzzy inference system that tracks player movement, which greatly improves variation of defender positioning, alongside an N-gram pattern recognition-based algorithm that predicts the next action of the attacking player. Analysis of implementation complexity and execution overhead shows that these techniques are not prohibitively expensive in either respect, and are therefore appropriate for use in games

A similarity-based inference engine for non-singleton fuzzy logic systems

In non-singleton fuzzy logic systems (NSFLSs) input uncertainties are modelled with input fuzzy sets in order to capture input uncertainty such as sensor noise. The performance of NSFLSs in handling such uncertainties depends both on the actual input fuzzy sets (and their inherent model of uncertainty) and on the way that they affect the inference process. This paper proposes a novel type of NSFLS by replacing the composition-based inference method of type-1 fuzzy relations with a similarity-based inference method that makes NSFLSs more sensitive to changes in the input's uncertainty characteristics. The proposed approach is based on using the Jaccard ratio to measure the similarity between input and antecedent fuzzy sets, then using the measured similarity to determine the firing strength of each individual fuzzy rule. The standard and novel approaches to NSFLSs are experimentally compared for the well-known problem of Mackey-Glass time series predictions, where the NSFLS's inputs have been perturbed with different levels of Gaussian noise. The experiments are repeated for system training under both noisy and noise-free conditions. Analyses of the results show that the new method outperforms the standard approach by substantially reducing the prediction errors