A hybrid stock trading system for intelligent technical analysis-based equivolume charting

We describe in this book, new methods for building intelligent systems using type-2 fuzzy logic and soft computing techniques. Soft Computing (SC) consists of several computing paradigms, including type-1 fuzzy logic, neural networks, and genetic algorithms, which can be used to create powerful hybrid intelligent systems. In this book, we are extending the use of fuzzy logic to a higher order, which is called type-2 fuzzy logic. Combining type-2 fuzzy logic with traditional SC techniques, we can build powerful hybrid intelligent systems that can use the advantages that each technique offers. We consider in this book the use of type-2 fuzzy logic and traditional SC techniques to solve pattern recognition problems in real-world applications. We consider in particular the problems of face, fingerprint and voice recognition. We also consider the problem of recognizing a person by integrating the information given by the face, fingerprint and voice of the person. Other types of applications solved with type-2 fuzzy logic and SC techniques, include intelligent control, intelligent manufacturing, and adaptive noise cancellation.

Summary form only given. There has been substantial progress in both intelligent systems and robotics in recent years. While in the past robots were most successful in traditional industrial environments, new generations of hybrid intelligent robotic systems are being developed which focus on higher cognitive capabilities, including reasoning learning and language communication. In this paper we give an overview of learning neural robots from a perspective of integrative hybrid intelligent systems and illustrate some new developments including also examples under development in the Centre for Hybrid Intelligent Systems.

BackgroundExpert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication.ResultsA novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet.ConclusionThis paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer.

The fourth edition of this bestselling textbook explains the principles of artificial intelligence (AI) and its practical applications. Using clear and concise language, it provides a solid grounding across the full spectrum of AI techniques, so that its readers can implement systems in their own domain of interest. The coverage includes knowledge-based intelligence, computational intelligence (including machine learning), and practical systems that use a combination of techniques. All the key techniques of AI are explained—including rule-based systems, Bayesian updating, certainty theory, fuzzy logic (types 1 and 2), agents, objects, frames, symbolic learning, case-based reasoning, genetic algorithms and other optimization techniques, shallow and deep neural networks, hybrids, and the Lisp, Prolog, and Python programming languages. The book also describes a wide range of practical applications in interpretation and diagnosis, design and selection, planning, and control. Fully updated and revised, Intelligent Systems for Engineers and Scientists: A Practical Guide to Artificial Intelligence, Fourth Edition features: A new chapter on deep neural networks, reflecting the growth of machine learning as a key technique for AI A new section on the use of Python, which has become the de facto standard programming language for many aspects of AI The rule-based and uncertainty-based examples in the book are compatible with the Flex toolkit by Logic Programming Associates (LPA) and its Flint extension for handling uncertainty and fuzzy logic. Readers of the book can download this commercial software for use free of charge. This resource and many others are available at the author’s website: adrianhopgood.com. Whether you are building your own intelligent systems, or you simply want to know more about them, this practical AI textbook provides you with detailed and up-to-date guidance.

With artificial intelligence (AI) systems entering our working and leisure environments with increasing adaptation and learning capabilities, new opportunities arise for developing hybrid (human-AI) intelligence (HI) systems, comprising new ways of collaboration. However, there is not yet a structured way of specifying design solutions of collaboration for hybrid intelligence (HI) systems and there is a lack of best practices shared across application domains. We address this gap by investigating the generalization of specific design solutions into design patterns that can be shared and applied in different contexts. We present a human-centered bottom-up approach for the specification of design solutions and their abstraction into team design patterns. We apply the proposed approach for 4 concrete HI use cases and show the successful extraction of team design patterns that are generalizable, providing re-usable design components across various domains. This work advances previous research on team design patterns and designing applications of HI systems.

Today, Photovoltaic (PV) systems are very popular for power generation because they enjoy the advantage of being pollution free abundant natural resource, low maintenance cost and noiseless operation. Photovoltaic (PV) systems needs maximum power point tracking (MPPT) to maximize the PV array power output which is independent of the temperature, irradiation coPIC18F452nditions and electrical characteristics of the load. The Research Work introduces photovoltaic MPPT system based on PIC18F452 to achieve maximum power tracking. Moreover, the combination of boost circuit and PIC18F452 microcontroller is used for tracking of the maximum power generation from PV system. Moreover, Research presents a practical implementation of Perturbation and Observation (P&O), Incremental Conductance (IncCon) and Fuzzy Logic algorithms to achieve MPPT control. These algorithms are widely used because of its low-cost and ease of realization. Proposed P&O, Incremental Conductance and Fuzzy Logic algorithms are implemented and tested under different loads, and the test results are analyzed and compared. The results show the performance of the Fuzzy Logic and Incremental Conductance algorithm in tracking MPP is better than the P&O algorithm and the experimental results of Fuzzy Logic and Incremental Conductance algorithm indicate the feasibility and improved system functionality with high-efficiency. Also, results concludes that the design can effectively accomplish the purpose of the maximum power tracking.

An intelligent hybrid approach has been developed to integrate various stages in total design, including formulation of product design specifications, conceptual design, detail design, and manufacture. The integration is achieved by blending multiple artificial intelligence (AI) techniques and CAD/CAE/CAM into a single environment. It has been applied into power transmission system design. In addition to knowledge-based systems and artificial neural networks, another AI technique, genetic algorithms (GAs), are involved in the approach. The GA is used to conduct optimization tasks: (1) searching the best combination of design parameters to obtain optimum design of gears, and (2) optimization of the architecture of the artificial neural networks used in the hybrid system. In this paper, after a brief overview of the intelligent hybrid system, the GA applications are described in detail.

Today, there's a synergy beginning to form among neural nets (NNs), fuzzy logic (FL) and genetic algorithms (GAs). This paper reviews developments in this respect. Many designs of knowledge-based and associated learning systems using the combination of NNs and FL abilities have been presented. Some methods for the integration of GAs with fuzzy systems are described. Afterwards, work on hybrid systems of GAs and NNs is discussed. Finally, some advantages obtained through the fusion of the FL, NNs and GAs are emphasized, and possible ways of combining the three are developed.

This research paper focuses on the use of advanced hybrid intelligent systems for modeling, simulation, and control of complex systems with non-linear behavior. Non-linear dynamical systems, which are prevalent in various industries, present unique challenges that require sophisticated solutions. Hybrid intelligent systems, combining multiple innovative techniques from the field of Soft Computing, have shown great promise in addressing these challenges. In this paper, we provide a comprehensive overview of hybrid intelligent systems and their advantages in dealing with non-linear dynamical systems. We explore the integration of different Soft Computing methodologies, such as Neural Networks, Fuzzy Logic, Genetic Algorithms, and Chaos Theory, to create powerful hybrid systems. We present real-world case studies and experimental results to showcase the effectiveness of these hybrid systems in modeling, simulation, and control tasks. Finally, we discuss future research directions and challenges in this exciting field, emphasizing the importance of continued exploration and development of hybrid intelligent systems for non-linear dynamical systems.

Identification of two-phase water–air flow patterns in a vertical pipe using fuzzy logic and genetic algorithm

Conventionally modelling and simulation of complex nonlinear systems has been to construct a mathematical model and examine the system’s evolution or its control. This kind of approach can fail for many of the very large non-linear and complex systems being currently studied. With the invention of new advanced high-speed computers and the application of artificial intelligence paradigms new techniques have become available. Particularly neural networks and fuzzy logic for nonlinear modelling and genetic algorithms [Goldberg, D. (1989)] and evolutionary algorithms for optimisation methods have created new opportunities to solve complex systems [Bai, Y., Zhuang H. and Wang, D. (2006)]. This paper considers issues in design of multi-layer and hierarchical fuzzy logic systems. It proposes a decomposition technique for complex systems into hierarchical and multi-layered fuzzy logic sub-systems. The learning of fuzzy rules and internal parameters in a supervised manner is performed using genetic algorithms. The decomposition of complex nonlinear systems into hierarchical and multi-layered fuzzy logic sub-systems reduces greatly the number of fuzzy rules to be defined and improves the learning speed for such systems. In this paper a method for combining subsystems to create a hierarchical and multilayer fuzzy logic system is also described. Application areas considered are - the prediction of interest rate, unemployment rate predication and electricity usage prediction. Genetic Algorithms can be used as a tool for design and generation of fuzzy rules for a fuzzy logic system. This automatic design and generation of fuzzy rules, via genetic algorithms, can be categorised into two learning techniques namely, supervised and unsupervised. In supervised learning there are two distinct phases to the operation. In the first phase each individual is assessed based on the input signal that is propagated through the system producing output respond. The actual respond produced is then compared with a desired response, generating error signals that are then used as the fitness for the individual in the population of genetic algorithms. Supervised learning has successfully applied to solve some difficult problems. In this paper design and development of a genetic algorithm based supervised learning for fuzzy models with application to several problems is considered. A hybrid integrated architecture incorporating fuzzy logic and genetic algorithm can generate fuzzy rules that can be used in a fuzzy logic system for modelling, control and prediction

Design of efficient control algorithms for autonomous mobile robot movement in unknown and changing environment with obstacles and walls is a difficult task. There exist different strategies to design control systems to perform the robot movement. In this article possibility to use fuzzy logic, if-then rules, and genetic algorithm for autonomous mobile robot control is presented. Control using fuzzy logic is softer and better then control using if-then rules because of absence of motors speed jumps. It is shown that the rough control system can be designed using an expert knowledge. Then genetic algorithm can be used to improve the quality of the control system.

Braking system plays a major safety feature in any vehicle; it is considered as one of the key points in safe motoring. The main objective of a good braking system is to provide safe stopping, in addition to the fact that it provides repeatable stopping. Anti-lock Braking System (ABS) is one of the recently developed safety feature used in a vehicle in order to prevent wheels from locking. This article proposes an intelligent adaptive fuzzy logic (FL) genetic algorithm (GA) controller for the Anti-Lock Braking System (ABS) to control the braking of a vehicle by controlling the slip ratio based on quarter car model. The main objective of the proposed controller is to prevent wheels from locking, which is a major safety issue in any braking system for different road conditions. The proposed controller consists of a Fuzzy Logic (FL) Genetic Algorithm (GA) based controller. The Genetic Algorithm is utilized in tuning the membership functions of the Fuzzy logic controller, which is developed to calculate the amount of reduction of the applied torque due to applying brake based on the error of the measured slip ratio signal compared with the desired one. Then a Genetic Algorithm (GA) is utilized in tuning the Fuzzy Logic parameters, which are the center of the Gaussian membership function, and the spread (б). The simulation results of the proposed fuzzy logic genetic algorithm controller are compared with both the results of the Fuzzy logic controller and the PD controller developed in previous work of the authors. The simulation results present many advantages of using Fuzzy Logic Genetic Algorithm based controller over both the Fuzzy Logic and the PD controllers, and the simulation is carried out at different road conditions, namely dry, wet and icy road surface.

A power transformer needs continuous monitoring and fast protection as it is a very expensive piece of equipment and an essential element in an electrical power system. The most common protection technique used is the percentage differential logic, which provides discrimination between an internal fault and different operating conditions. Unfortunately, there are some operating conditions of power transformers that can mislead the conventional protection affecting the power system stability negatively. This study proposes the development of a new algorithm to improve the protection performance by using fuzzy logic, artificial neural networks and genetic algorithms. An electrical power system was modelled using Alternative Transients Program software to obtain the operational conditions and fault situations needed to test the algorithm developed, as well as a commercial differential relay. Results show improved reliability, as well as a fast response of the proposed technique when compared with conventional ones.

Artificial intelligence (AI) is nowadays an upcoming technology. It is a practice of simulating human intelligence for varied applications. When compared with the standard practices, AI is developing at a rapid rate. AI has proved its worth in several areas such as agriculture, automobile industry, banking and finance, space exploration, artificial creativity, etc. Owing to the efficiency, speed, and independence from human operations, AI is now entering the wastewater treatment sector. This technology has been used for monitoring the performance of the water treatment plants in terms of efficiency parameters, Biological Oxygen Demand (BOD) and Chemical Oxygen Demand (COD) determination, elimination of nitrogen and sulphur, prediction of turbidity and hardness, uptake of contaminants, etc., in the wastewater sector. Artificial Neural Networks (ANN), Fuzzy Logic Algorithms (FL), and Genetic Algorithms (GA) are the basic three models under AI predominantly used in the wastewater sector. Studies reveal that the determination coefficient values of 0.99 can be attained for COD, BOD, heavy metals and organics removal using ANN and hybrid intelligent systems. This review paper describes research with all the possible models of AI utilized in the water treatment which have enhanced the pollutant removal percentage accuracy of ranging from 84% to 90% and provided viewpoint on future directions of novel research, in the field with due focus on pollution remediation, cost effectiveness, energy economy, and water management.