This paper introduces a new type of fuzzy inference systems, denoted as dynamic evolving neural-fuzzy inference system (DENFIS), for adaptive online and offline 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 Takagi-Sugeno-type fuzzy rule set for a DENFIS online model; and (2) creation of a first-order Takagi-Sugeno-type fuzzy rule set, or an expanded high-order one, for a DENFIS offline model. A set of fuzzy rules can be inserted into DENFIS before or during its learning process. Fuzzy rules can also be extracted during or after the learning process. An evolving clustering method (ECM), which is employed in both online and offline 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.

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DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction

Here we address the problem of parameter estimation (inverse problem) of nonlinear dynamic biochemical pathways. This problem is stated as a nonlinear programming (NLP) problem subject to nonlinear differential-algebraic constraints. These problems are known to be frequently ill-conditioned and multimodal. Thus, traditional (gradient-based) local optimization methods fail to arrive at satisfactory solutions. To surmount this limitation, the use of several state-of-the-art deterministic and stochastic global optimization methods is explored. A case study considering the estimation of 36 parameters of a nonlinear biochemical dynamic model is taken as a benchmark. Only a certain type of stochastic algorithm, evolution strategies (ES), is able to solve this problem successfully. Although these stochastic methods cannot guarantee global optimality with certainty, their robustness, plus the fact that in inverse problems they have a known lower bound for the cost function, make them the best available candidates.

/pdf/parameter-estimation-in-biochemical-pathways-a-comparison-of-2a8v24rhva.pdf

Parameter Estimation in Biochemical Pathways: A Comparison of Global Optimization Methods

In this paper, we combine a new approach for linear time delay systems based on a descriptor representation with a recent result on bounding of cross products of vectors. A delay-dependent criterion for determining the stability of systems with time-varying delays is obtained. This criterion is used to derive an efficient stabilizing state-feedback design method for systems with parameter uncertainty, of either the polytopic or the norm-bounded types.

/pdf/an-improved-stabilization-method-for-linear-time-delay-4dp4tdg8bn.pdf

An improved stabilization method for linear time-delay systems

Huntington's disease (HD) is a neurodegenerative disorder caused by an unstable CAG repeat. For patients at risk, participating in predictive testing and learning of having CAG expansion, a major unanswered question shifts from "Will I get HD?" to "When will it manifest?" Using the largest cohort of HD patients analyzed to date (2913 individuals from 40 centers worldwide), we developed a parametric survival model based on CAG repeat length to predict the probability of neurological disease onset (based on motor neurological symptoms rather than psychiatric onset) at different ages for individual patients. We provide estimated probabilities of onset associated with CAG repeats between 36 and 56 for individuals of any age with narrow confidence intervals. For example, our model predicts a 91% chance that a 40-year-old individual with 42 repeats will have onset by the age of 65, with a 95% confidence interval from 90 to 93%. This model also defines the variability in HD onset that is not attributable to CAG length and provides information concerning CAG-related penetrance rates.

A new model for prediction of the age of onset and penetrance for Huntington's disease based on CAG length.

Power System Oscillations.- Linear Control in Power Systems.- Test System Model.- Power System Stabilizers.- Multiple-Model Adaptive Control Approach.- Simultaneous Stabilization.- Mixed-Sensitivity Approach Using Linear Matrix Inequalities.- Normalized ?? Loop-Shaping Using Linear Matrix Inequalities.- ?? Control For Time-Delayed Systems.

Robust Control in Power Systems

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/pdf/optimization-toolbox-user-s-guide-b2064iob0t.pdf

Andrew Grace

Papers

Optimization Toolbox User's Guide

Optimization toolbox : for use with MATLAB