Self learning neuro-fuzzy modeling using hybrid genetic probabilistic approach for engine air/fuel ratio prediction

Machine Learning is concerned in constructing models which can learn and make predictions based on data. Rule extraction from real world data that are usually tainted with noise, ambiguity, and uncertainty, automatically requires feature selection. Neuro-Fuzzy system (NFS) which is known with its prediction performance has the difficulty in determining the proper number of rules and the number of membership functions for each rule. An enhanced hybrid Genetic Algorithm based Fuzzy Bayesian classifier (GA-FBC) was proposed to help the NFS in the rule extraction. Feature selection was performed in the rule level overcoming the problems of the FBC which depends on the frequency of the features leading to ignore the patterns of small classes. As dealing with a real world problem such as the Air/Fuel Ratio (AFR) prediction, a multi-objective problem is adopted. The GA-FBC uses mutual information entropy, which considers the relevance between feature attributes and class attributes. A fitness function is proposed to deal with multi-objective problem without weight using a new composition method. The model was compared to other learning algorithms for NFS such as Fuzzy c-means (FCM) and grid partition algorithm. Predictive accuracy and the complexity of the Fuzzy Rule Base System (FRBS) including number of rules and number of terms in each rule were taken as terms of evaluation. It was also compared to the original GA-FBC depending on the frequency not on Mutual Information (MI). Experimental results using Air/Fuel Ratio (AFR) data sets show that the new model participates in decreasing the average number of attributes in the rule and sometimes in increasing the average performance compared to other models. This work facilitates in achieving a self-generating FRBS from real data. The GA-FBC can be used as a new direction in machine learning research. This research contributes in controlling automobile emissions in helping the reduction of one of the most causes of pollution to produce greener environment

3D terrain generation using neural networks

With the increase in computation power, coupled with the advancements in the field in the form of GANs and cGANs, Neural Networks have become an attractive proposition for content generation. This opened opportunities for Procedural Content Generation algorithms (PCG) to tap Neural Networks generative power to create tools that allow developers to remove part of creative and developmental burden imposed throughout the gaming industry, be it from investors looking for a return on their investment and from consumers that want more and better content, fast. This dissertation sets out to develop a PCG mixed-initiative tool, leveraging cGANs, to create authored 3D terrains, allowing users to directly influence the resulting generated content without the need for formal training on terrain generation or complex interactions with the tool to influence the generative output, as opposed to state of the art generative algorithms that only allow for random content generation or are needlessly complex. Testing done to 113 people online, as well as in-person testing done to 30 people, revealed that it is indeed possible to develop a tool that allows users from any level of terrain creation knowledge, and minimal tool training, to easily create a 3D terrain that is more realistic looking than those generated by state-of-the-art solutions such as Perlin Noise.Com o aumento do poder de computação, juntamente com os avanços neste campo na forma de GANs e cGANs, as Redes Neurais tornaram-se numa proposta atrativa para a geração de conteúdos. Graças a estes avanços, abriram-se oportunidades para os algoritmos de Geração de Conteúdos Procedimentais(PCG) explorarem o poder generativo das Redes Neurais para a criação de ferramentas que permitam aos programadores remover parte da carga criativa e de desenvolvimento imposta em toda a indústria dos jogos, seja por parte dos investidores que procuram um retorno do seu investimento ou por parte dos consumidores que querem mais e melhor conteúdo, o mais rápido possível. Esta dissertação pretende desenvolver uma ferramenta de iniciativa mista PCG, alavancando cGANs, para criar terrenos 3D cocriados, permitindo aos utilizadores influenciarem diretamente o conteúdo gerado sem necessidade de terem formação formal sobre a criação de terrenos 3D ou interações complexas com a ferramenta para influenciar a produção generativa, opondo-se assim a algoritmos generativos comummente utilizados, que apenas permitem a geração de conteúdo aleatório ou que são desnecessariamente complexos. Um conjunto de testes feitos a 113 pessoas online e a 30 pessoas presencialmente, revelaram que é de facto possível desenvolver uma ferramenta que permita aos utilizadores, de qualquer nível de conhecimento sobre criação de terrenos, e com uma formação mínima na ferramenta, criar um terreno 3D mais realista do que os terrenos gerados a partir da solução de estado da arte, como o Perlin Noise, e de uma forma fácil

Ontology-based transformation of natural language queries into SPARQL queries by evolutionary algorithms

In dieser Arbeit wird ein ontologiegetriebenes evolutionäres Lernsystem für natürlichsprachliche Abfragen von RDF-Graphen vorgestellt. Das lernende System beantwortet die Anfrage nicht selbst, sondern generiert eine SPARQL-Abfrage gegen die Datenbank. Zu diesem Zweck wird das Evolutionary Dataflow Agents Framework eingeführt, ein allgemeines Lernsystem, das auf der Grundlage evolutionärer Algorithmen Agenten erzeugt, die lernen, ein Problem zu lösen. Die Hauptidee des Frameworks ist es, Probleme zu unterstützen, die einen mittelgroßen Suchraum (Anwendungsfall: Analyse von natürlichsprachlichen Abfragen) von streng formal strukturierten Lösungen (Anwendungsfall: Synthese von Datenbankabfragen) mit eher lokalen klassischen strukturellen und algorithmischen Aspekten kombinieren. Dabei kombinieren die Agenten lokale algorithmische Funktionalität von Knoten mit einem flexiblen Datenfluss zwischen den Knoten zu einem globalen Problemlösungsprozess. Grob gesagt gibt es Knoten, die Informationsfragmente generieren, indem sie Eingabedaten und/oder frühere Fragmente kombinieren, oft unter Verwendung von auf Heuristik basierenden Vermutungen. Andere Knoten kombinieren, sammeln und reduzieren solche Fragmente auf mögliche Lösungen und grenzen diese auf die endgültige Lösung ein. Zu diesem Zweck werden die Informationen von den Agenten weitergegeben. Die Konfiguration dieser Agenten, welche Knoten sie kombinieren und wohin genau die Daten fließen, ist Gegenstand des Lernens. Das Training beginnt mit einfachen Agenten, die - wie in Lern-Frameworks üblich - eine Reihe von Aufgaben lösen und dafür bewertet werden. Da die erzeugten Antworten in der Regel komplexe Strukturen aufweisen, setzt das Framework einen neuartigen feinkörnigen energiebasierten Bewertungs- und Auswahlschritt ein. Die ausgewählten Agenten bilden dann die Grundlage für die Population der nächsten Runde. Die Evolution wird wie üblich durch Mutationen und Agentenfusion gewährleistet. Als Anwendungsfall wurde EvolNLQ implementiert, ein System zur Beantwortung natürlichsprachlicher Abfragen gegen RDF-Datenbanken. Hierfür wird die zugrundeliegende Ontologie medatata (extern) algorithmisch vorverarbeitet. Für die Agenten werden geeignete Datenelementtypen und Knotentypen definiert, die die Prozesse der Sprachanalyse und der Anfragesynthese in mehr oder weniger elementare Operationen zerlegen. Die "Größe" der Operationen wird bestimmt durch die Grenze zwischen Berechnungen, d.h. rein algorithmischen Schritten (implementiert in einzelnen mächtigen Knoten) und einfachen heuristischen Schritten (ebenfalls realisiert durch einfache Knoten), und freiem Datenfluss, der beliebige Verkettungen und Verzweigungskonfigurationen der Agenten erlaubt. EvolNLQ wird mit einigen anderen Ansätzen verglichen und zeigt konkurrenzfähige Ergebnisse.In this thesis an ontology-driven evolutionary learning system for natural language querying of RDF graphs is presented. The learning system itself does not answer the query, but generates a SPARQL query against the database. For this purpose, the Evolutionary Dataflow Agents framework, a general learning framework is introduced that, based on evolutionary algorithms, creates agents that learn to solve a problem. The main idea of the framework is to support problems that combine a medium-sized search space (use case: analysis of natural language queries) of strictly, formally structured solutions (use case: synthesis of database queries), with rather local classical structural and algorithmic aspects. For this, the agents combine local algorithmic functionality of nodes with a flexible dataflow between the nodes to a global problem solving process. Roughly, there are nodes that generate informational fragments by combining input data and/or earlier fragments, often using heuristics-based guessing. Other nodes combine, collect, and reduce such fragments towards possible solutions, and narrowing these towards the unique final solution. For this, informational items are floating through the agents. The configuration of these agents, what nodes they combine, and where exactly the data items are flowing, is subject to learning. The training starts with simple agents, which –as usual in learning frameworks– solve a set of tasks, and are evaluated for it. Since the produced answers usually have complex structures answers, the framework employs a novel fine-grained energy-based evaluation and selection step. The selected agents then are the basis for the population of the next round. Evolution is provided as usual by mutations and agent fusion. As a use case, EvolNLQ has been implemented, a system for answering natural language queries against RDF databases. For this, the underlying ontology medatata is (externally) algorithmically preprocessed. For the agents, appropriate data item types and node types are defined that break down the processes of language analysis and query synthesis into more or less elementary operations. The "size" of operations is determined by the border between computations, i.e., purely algorithmic steps (implemented in individual powerful nodes) and simple heuristic steps (also realized by simple nodes), and free dataflow allowing for arbitrary chaining and branching configurations of the agents. EvolNLQ is compared with some other approaches, showing competitive results.2022-01-2

Fuzzy Logic

Fuzzy Logic is becoming an essential method of solving problems in all domains. It gives tremendous impact on the design of autonomous intelligent systems. The purpose of this book is to introduce Hybrid Algorithms, Techniques, and Implementations of Fuzzy Logic. The book consists of thirteen chapters highlighting models and principles of fuzzy logic and issues on its techniques and implementations. The intended readers of this book are engineers, researchers, and graduate students interested in fuzzy logic systems

Numerical and Evolutionary Optimization 2020

This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications

Evolutionary algorithms for practical sensor fault tolerant control

The Shaky Hand is a multi-input, multi-output laboratory demonstrator which is modelled on a village fete game. In the original, the aim is to guide, by hand, a wire loop along a wire which has been bent to form a meandering track, 'without touching the loop to the wire. In the original game, touching the hand-held loop against the wire track sets off a loud warning bell and the player loses. The thesis presents the research work associated with the quest for practical solutions to a generic problem: the correct operation of a fallible system. The work covers three distinct areas: modelling of the demonstrator, design and construction of a physical system, and evoiution of algorithms for control of the demonstrator in practice in the presence of sensor faults, using Cartesian Genetic Programming (CGP). The third area forms the core of the thesis. The key challenges in creating the virtual environment to train for generic sensor fault tolerant algorithms are considered and addressed. The evolved algorithms are analysed and then verified using the demonstrator in practice. The practical results showed that sensor fault tolerant control was successfully achieved