Electrocardiogram time series forecasting and optimization using ant colony optimization algorithm

The aim of this work is to create the time series dynamic model, which is based on non-uniform embedding in the phase-space. To solve selection of time delays problem efficiently, this paper proposes an ant colony optimization (ACO) way. Firstly, false nearest neighbor method is used for determine the embedding dimension. Secondly, ant colony optimization algorithm is used for non-uniform time delay search. To quicken search speed, roulette wheel selection algorithm distributes ants’ pheromones. Optimization fitness function is the average area of all attractors. Obtained embeddings found by this model are applied in time-series forecasting using radial basis function neural networks. The study is presented in Mackey-Glass and electrocardiogram (ECG) time series forecasting. Prediction results show that the proposed model provides precise prediction accuracy

Electrocardiogram time series forecasting and optimization using ant colony optimization algorithm

The aim of this work is to create the time series dynamic model, which is based on non-uniform embedding in the phase-space. To solve selection of time delays problem efficiently, this paper proposes an ant colony optimization (ACO) way. Firstly, false nearest neighbor method is used for determine the embedding dimension. Secondly, ant colony optimization algorithm is used for non-uniform time delay search. To quicken search speed, roulette wheel selection algorithm distributes ants’ pheromones. Optimization fitness function is the average area of all attractors. Obtained embeddings found by this model are applied in time-series forecasting using radial basis function neural networks. The study is presented in Mackey-Glass and electrocardiogram (ECG) time series forecasting. Prediction results show that the proposed model provides precise prediction accuracy

DENFIS: Dynamic Evolving Neural-Fuzzy Inference System and its Application for Time Series Prediction

This paper introduces a new type of fuzzy inference systems, denoted as DENFIS (dynamic evolving neural-fuzzy inference system), for adaptive on-line and off-line learning, and their application for dynamic time series prediction. DENFIS evolve through incremental, hybrid (supervised/unsupervised), learning and accommodate new input data, including new features, new classes, etc. through local element tuning. New fuzzy rules are created and updated during the operation of the system. At each time moment the output of DENFIS is calculated through a fuzzy inference system based on m-most activated fuzzy rules which are dynamically chosen from a fuzzy rule set. Two approaches are proposed: (1) dynamic creation of a first-order TakagiSugeno type fuzzy rule set for a DENFIS on-line model; (2) creation of a first-order TakagiSugeno type fuzzy rule set, or an expanded high-order one, for a DENFIS off-line model. A set of fuzzy rules can be inserted into DENFIS before, or during its learning process. Fuzzy rules can also be extracted during the learning process or after it. An evolving clustering method (ECM), which is employed in both on-line and off-line DENFIS models, is also introduced. It is demonstrated that DENFIS can effectively learn complex temporal sequences in an adaptive way and outperform some well known, existing models

Universal approximation propriety of Flexible Beta Basis Function Neural Tree

Abstract — In this paper, the universal approximation propri

Superstructure optimization and forecasting of decentralized energy generation based on palm oil biomass

Malaysia realizes the importance of addressing the concern of energy security to accomplish the nation’s policy objectives by mitigating the issues of security, energy efficiency and environmental impacts. To meet the rising demand for energy and incorporation of Green Technology in the national policy, Malaysian government during the last three decades has developed several strategies and policies. National Green Technology Policy was an initiative, which marked the firm determination of the government to incorporate Green Technology in the nation’s economy policy. Malaysia has abundant biomass resources, especially oil palm residues with power generation potential of about 2400 MW, which is promising for decentralized electricity generation (DEG). The aim of this study is to determine the best location to install appropriate biomass electricity generation plant in Johor and forecasting the electricity market (i.e. electricity demand) in order to provide a strategic assessment of measures for the local energy planners of Malaysia, as an optimization bottom-up model. A superstructure was developed and optimized to represent DEG system. The problem was formulated as Mixed Integer Nonlinear Programming (MINLP) and implemented in General Algebraic Modeling System (GAMS). Electricity demand was modeled using Adaptive Neuro Fuzzy Inference System (ANFIS). Based on GAMS and ANFIS models, palm oil biomass based DEG system and distribution network scenarios for current as well as next ten, twenty and thirty years have been proposed for State of Johor, Malaysia. Biomass from sixty six Palm Oil Mills (POMs) would be collected and transported to eight selected locations. Empirical findings of this study suggested that total production cost is minimized by placing biomass gasification based integrated combine cycle (BIGCC) power plant of 50MW at all eight locations. For 2020 Scenario, no additional infrastructure will be required. For 2030 Scenario, additional units of BIGCC of 50MW will be required at five out of eight locations. While for 2040 Scenario, again no additional infrastructure development will be needed. Total minimum cost varied from 6.31 M$/yr for current scenario to 22.63 M$/yr for 2040 scenario

FuNN/2—a fuzzy neural network architecture for adaptive learning and knowledge acquisition

Please note that this is a searchable PDF derived via optical character recognition (OCR) from the original source document. As the OCR process is never 100% perfect, there may be some discrepancies between the document image and the underlying text.Fuzzy neural networks have several features that make them well suited to a wide range of knowledge engineering applications. These strengths include fast and accurate learning, good generalisation capabilities, excellent explanation facilities in the form of semantically meaningful fuzzy rules, and the ability to accommodate both data and existing expert knowledge about the problem under consideration. This paper investigates adaptive learning, rule extraction and insertion, and neural/fuzzy reasoning for a particular model of a fuzzy neural network called FuNN. As well as providing for representing a fuzzy system with an adaptable neural architecture, FuNN also incorporates a genetic algorithm in one of its adaptation modes. A version of FuNN—FuNN/2, which employs triangular membership functions and correspondingly modified learning and adaptation algorithms, is also presented in the paper.Unpublished[1] Yamakawa, T., Kusanagi, H., Uchino, E. and Miki, T., “A new Effective Algorithm for Neo Fuzzy Neuron Model”, in: Proceedings of Fifth IFSA World Congress, (1993) 1017-1020. [2] Hashiyama, T., Furuhashi, T., Uchikawa, Y., “A Decision Making Model Using a Fuzzy Neural Network”, in: Proceedings of the 2nd International Conference on Fuzzy Logic & Neural Networks, Iizuka, Japan, (1992) 1057-1060. [3] Kasabov, N., “Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems”, Fuzzy Sets and Systems, 82 (2), 1996, 135-149 [4] Kasabov, N., Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering, The MIT Press, CA, MA, 1996 [5] Kasabov, N., “Adaptable connectionist production systems”. Neurocomputing, 13 (2-4), 1996, 95-117 [6] Hauptmann, W., Heesche, K., A Neural Net Topology for Bidirectional Fuzzy-Neuro Transformation, in: Proceedings of the FUZZ-IEEE/IFES, Yokohama, Japan, (1995) 1511-1518. [7] Jang, R., ANFIS: adaptive network-based fuzzy inference system, IEEE Trans. on Syst.,Man, Cybernetics, 23(3), May-June 1993, 665-685 [8] Lin, C-T., Lin, C-J., Lee, C.T., Fuzzy adaptive learning control network with on-line learning, Fuzzy Sets and Systems, 71(1), 1995, 25-45 [9] Goldberg, D., Genetic Algorithms is Search, Optimization and Machine Learning, Addison Wesley, 1989 [10] Mang, G. Lan, H., Zhang, L. “A Genetic-based method of Generating Fuzzy Rules and Membership Functions by Learning from Examples”, in: Proceedings of International Conference on Neural Information Processing (ICONIP ’95) Volume One, 1995, 335-338 [11] Kasabov, N. Hybrid Connectionist Fuzzy Production Systems - Towards Building Comprehensive AI, Intelligent Automation and Soft Computing, 1:4 (1995) 351-360) [12] Carpenter, G., “Learning, Recognition, and Prediction by ART and ARTMAP Neural Networks”, in: Proceedings of ICNN’96, IEEE Press, Volume “Plenary Panel and Special Sessions, 1996, 244-249 [13] Kasabov, N., Investigating the adaptation and forgetting in fuzzy neural networks by using the method of training and zeroing”, in: Proceedings of the International Conference on Neural Networks ICNN’96, Plenary, Panel and and Special Sessions volume,1996, 118-123 [14] Kasabov, N., Advanced Neuro-Fuzzy Engineering for Building Intelligent Adaptive Information Systems, in: L.Reznick, V.Dimitrov, J.Kacprzyk (eds.) Fuzzy Systems Design: Social and Engineering Applications, Physica-Verlag (Springer Verlag), to appear in 199

Control of Real Mobile Robot Using Artificial Intelligence Technique

An eventual objective of mobile robotics research is to bestow the robot with high cerebral skill, of which navigation in an unfamiliar environment can be succeeded by using on‐line sensory information, which is essentially starved of humanoid intermediation. This research emphases on mechanical design of real mobile robot, its kinematic & dynamic model analysis and selection of AI technique based on perception, cognition, sensor fusion, path scheduling and analysis, which has to be implemented in robot for achieving integration of different preliminary robotic behaviors (e.g. obstacle avoidance, wall and edge following, escaping dead end and target seeking). Navigational paths as well as time taken during navigation by the mobile robot can be expressed as an optimization problem and thus can be analyzed and solved using AI techniques. The optimization of path as well as time taken is based on the kinematic stability and the intelligence of the robot controller. A set of linguistic fuzzy rules are developed to implement expert knowledge under various situations. Both of Mamdani and Takagi-Sugeno fuzzy model are employed in control algorithm for experimental purpose. Neural network has also been used to enhance and optimize the outcome of controller, e.g. by introducing a learning ability. The cohesive framework combining both fuzzy inference system and neural network enabled mobile robot to generate reasonable trajectories towards the target. An authenticity checking has been done by performing simulation as well as experimental results which showed that the mobile robot is capable of avoiding stationary obstacles, escaping traps, and reaching the goal efficiently

An Intelligent System for Induction Motor Health Condition Monitoring

Induction motors (IMs) are commonly used in both industrial applications and household appliances. An IM online condition monitoring system is very useful to identify the IM fault at its initial stage, in order to prevent machinery malfunction, decreased productivity and even catastrophic failures. Although a series of research efforts have been conducted over decades for IM fault diagnosis using various approaches, it still remains a challenging task to accurately diagnose the IM fault due to the complex signal transmission path and environmental noise. The objective of this thesis is to develop a novel intelligent system for more reliable IM health condition monitoring. The developed intelligent monitor consists of two stages: feature extraction and decision-making. In feature extraction, a spectrum synch technique is proposed to extract representative features from collected stator current signals for fault detection in IM systems. The local bands related to IM health conditions are synchronized to enhance fault characteristic features; a central kurtosis method is suggested to extract representative information from the resulting spectrum and to formulate an index for fault diagnosis. In diagnostic pattern classification, an innovative selective boosting technique is proposed to effectively classify representative features into different IM health condition categories. On the other hand, IM health conditions can also be predicted by applying appropriate prognostic schemes. In system state forecasting, two forecasting techniques, a model-based pBoost predictor and a data-driven evolving fuzzy neural predictor, are proposed to forecast future states of the fault indices, which can be employed to further improve the accuracy of IM health condition monitoring. A novel fuzzy inference system is developed to integrate information from both the classifier and the predictor for IM health condition monitoring. The effectiveness of the proposed techniques and integrated monitor is verified through simulations and experimental tests corresponding to different IM states such as IMs with broken rotor bars and with the bearing outer race defect. The developed techniques, the selective boosting classifier, pBoost predictor and evolving fuzzy neural predictor, are effective tools that can be employed in a much wider range of applications. In order to select the most reliable technique in each processing module so as to provide a more positive assessment of IM health conditions, some more techniques are also proposed for each processing purpose. A conjugate Levebnerg-Marquardt method and a Laplace particle swarm technique are proposed for model parameter training, whereas a mutated particle filter technique is developed for system state prediction. These strong tools developed in this work could also be applied to fault diagnosis and other applications