2007 IEEE International Conference on Automation Science and Engineering, 2007
Abstract We consider the problem of modeling the biological activity of inhibitors (belonging to ... more Abstract We consider the problem of modeling the biological activity of inhibitors (belonging to the paullone family) for Glycogen Synthase Kinase-3 (GSK-3). The development of a nonlinear model that establishes the quantitative structure-activity relationship (QSAR) ...
VLSI Design 2000. Wireless and Digital Imaging in the Millennium. Proceedings of 13th International Conference on VLSI Design
An important aspect of hardware-software co-design is partitioning of tasks to be scheduled on th... more An important aspect of hardware-software co-design is partitioning of tasks to be scheduled on the hardware and software resources. Existing approaches separate partitioning and scheduling in two steps. Since partitioning solutions affect scheduling results and vice versa, the existing sequential approaches may lead to sub-optimal results. In this paper, we present an integrated hardware/software scheduling, partitioning and binding strategy. We use dynamic programming techniques to devise an optimal solution for partitioning of a given concurrent task graph, which models the co-design problem, for execution on one software (single CPU) and several hardware resources (multiple FPGA's), with the objective of minimizing the total execution time. Our implementation shows that we can solve problem instances where the task graph has 40 nodes and 600 edges in less than a second.
2011 IEEE International Conference on Automation Science and Engineering, 2011
The combination of fuzzy models could be an ef- fective way to improve system performance. This t... more The combination of fuzzy models could be an ef- fective way to improve system performance. This text proposes a fuzzy approach to the combination of fuzzy models, i.e., the different fuzzy models are combined using a fuzzy rule- based model. The combining fuzzy model is identified using an algorithm that is stable towards disturbances. The combination approach provides simultaneously the
This study considers the problem of Robust Fuzzy approximation of a time-varying nonlinear proces... more This study considers the problem of Robust Fuzzy approximation of a time-varying nonlinear process in the presence of uncertainties in the identification data using a Sugeno Fuzzy System while maintaining the interpretability of the fuzzy model during identification. A recursive procedure for the estimation of fuzzy parameters is proposed based on solving local optimization problem that attempt to minimize the
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2006
This study is concerned with the adaptive learning of an interpretable Sugeno-type fuzzy inferenc... more This study is concerned with the adaptive learning of an interpretable Sugeno-type fuzzy inference system, in a deterministic framework, in the presence of data uncertainties and modeling errors. The authors explore the use of Hinfinity estimation theory and least squares estimation for online learning of membership functions and consequent parameters without making any assumption and requiring a priori knowledge of upper bounds, statistics, and distribution of data uncertainties and modeling errors. The issues of data uncertainties, modeling errors, and time variations have been considered mathematically in a sensible way. The proposed robust approach to the adaptive learning of fuzzy models has been illustrated through the examples of adaptive system identification, time-series prediction, and estimation of an uncertain process.
Mental stress is accompanied by dynamic changes in autonomic nervous system (ANS) activity. Heart... more Mental stress is accompanied by dynamic changes in autonomic nervous system (ANS) activity. Heart rate variability (HRV) analysis is a popular tool for assessing the activities of autonomic nervous system. This paper presents a novel method of HRV analysis for mental stress assessment using fuzzy clustering and robust identification techniques. The approach consists of 1) online monitoring of heart rate
ABSTRACT Stationary fuzzy Fokker-Planck learning (SFFPL) is a recently introduced computational m... more ABSTRACT Stationary fuzzy Fokker-Planck learning (SFFPL) is a recently introduced computational method that applies fuzzy modeling to solve optimization problems. This study develops a concept of applying SFFPL-based computations for nonlinear constrained optimization. We consider the development of SFFPL-based optimization algorithms which do not require derivatives of the objective function and of the constraints. The sequential penalty approach was used to handle the inequality constraints. It was proved under some standard assumptions that the carefully designed SFFPL-based algorithms converge asymptotically to the stationary points. The convergence proofs follow a simple mathematical approach and invoke mean-value theorem. The algorithms were evaluated on the test problems with the number of variables up to 50. The performance comparison of the proposed algorithms with some of the standard optimization algorithms further justifies our approach. The SFFPL-based optimization approach, due to its novelty, could possibly be extended to several research directions.
We believe that nonlinear fuzzy filtering techniques may be turned out to give better robustness ... more We believe that nonlinear fuzzy filtering techniques may be turned out to give better robustness performance than the existing linear methods of estimation (2 and filtering techniques), because of the fact that not only linear parameters (consequents), but also the nonlinear parameters (membership functions) attempt to identify the uncertain behavior of the unknown system. However, the fuzzy identification methods must be robust to data uncertainties and modeling errors to ensure that the fuzzy approximation of unknown system's behavior is optimal in some sense. This study presents a deterministic approach to the robust design of fuzzy models in the presence of unknown but finite uncertainties in the identification data. We consider online identification of an interpretable fuzzy model, based on the robust solution of a regularized least-squares fuzzy parameters estimation problem. The aim is to resolve the difficulties associated with the robust fuzzy identification method due to lack of a priori knowledge about upper bounds on the data uncertainties. The study derives an optimal level of regularization that should be provided to ensure the robustness of fuzzy identification strategy by achieving an upper bound on the value of energy gain from data uncertainties and modeling errors to the estimation errors. A time-domain feedback analysis of the proposed identification approach is carried out with emphasis on stability, robustness, and steady-state issues. The simulation studies are provided to show the superiority of the proposed fuzzy estimation over the classical estimation methods.
This study considers the robust identification of the parameters describing a Sugeno type fuzzy i... more This study considers the robust identification of the parameters describing a Sugeno type fuzzy inference system with uncertain data. The objective is to minimize the worst-case residual error using a numerically efficient algorithm. The Sugeno type fuzzy systems are linear in consequent parameters but nonlinear in antecedent parameters. The robust consequent parameters identification problem can be formulated as second-order cone programming problem. The optimal solution of this second-order cone problem can be interpreted as solution of a Tikhonov regularization problem with a special choice of regularization parameter which is optimal for robustness (Ghaoui and Lebret (1997). SAIM Journal of Matrix Analysis and Applications 18, 1035-1064). The final regularized nonlinear optimization problem allowing simultaneous identification of antecedent and consequent parameters is solved iteratively using a generalized Gauss-Newton like method. To illustrate the approach, several simulation studies on numerical examples including the modelling of a spectral data function (one-dimensional benchmark example) is provided. The proposed robust fuzzy identification scheme has been applied to approximate the physical fitness of patients with a fuzzy expert system. The identified fuzzy expert system is shown to be capable of capturing the decisions (experiences) of a medical expert.
This study presents a new approach to adaptation of Sugeno type fuzzy inference systems using reg... more This study presents a new approach to adaptation of Sugeno type fuzzy inference systems using regularization, since regularization improves the robustness of standard parameter estimation algorithms leading to stable fuzzy approximation. The proposed method can be used for modelling, identification and control of physical processes. A recursive method for on-line identification of fuzzy parameters employing Tikhonov regularization is suggested. The power of approach was shown by applying it to the modelling, identification, and adaptive control problems of dynamic processes. The proposed approach was used for modelling of human-decisions (experience) with a fuzzy inference system and for the fuzzy approximation of physical fitness with real world medical data.
This study presents a method of adaptive identification of parameters describing Sugeno fuzzy inf... more This study presents a method of adaptive identification of parameters describing Sugeno fuzzy inference system in presence of bounded disturbances while maintaining the readability and interpretability of the fuzzy model during and after identification. This method do not require any a priori knowledge of a bound on the disturbance and noise and of a bound on the unknown parameters values. The method can be used for the robust and adaptive identification of slowly time varying nonlinear systems using fuzzy inference systems. The suggested method was used to build a fuzzy expert system that approximates the functional relationship between physical fitness and some of the measurable physiological parameters by their real measurements and opinion (human-experiences) of a medical expert.
This study introduces a fuzzy filtering based technique for rendering robustness to the modelling... more This study introduces a fuzzy filtering based technique for rendering robustness to the modelling methods. We consider a case study dealing with the development of a model for predicting the bioconcentration factor (BCF) of chemicals. The conventional neural/fuzzy BCF models, ...
A novel method for the robust identification of interpretable fuzzy models, based on the criterio... more A novel method for the robust identification of interpretable fuzzy models, based on the criterion that identification errors are least sensitive to data uncertainties and modelling errors, is suggested. The robustness of identification errors towards unknown disturbances (data ...
2007 IEEE International Conference on Automation Science and Engineering, 2007
Abstract We consider the problem of modeling the biological activity of inhibitors (belonging to ... more Abstract We consider the problem of modeling the biological activity of inhibitors (belonging to the paullone family) for Glycogen Synthase Kinase-3 (GSK-3). The development of a nonlinear model that establishes the quantitative structure-activity relationship (QSAR) ...
VLSI Design 2000. Wireless and Digital Imaging in the Millennium. Proceedings of 13th International Conference on VLSI Design
An important aspect of hardware-software co-design is partitioning of tasks to be scheduled on th... more An important aspect of hardware-software co-design is partitioning of tasks to be scheduled on the hardware and software resources. Existing approaches separate partitioning and scheduling in two steps. Since partitioning solutions affect scheduling results and vice versa, the existing sequential approaches may lead to sub-optimal results. In this paper, we present an integrated hardware/software scheduling, partitioning and binding strategy. We use dynamic programming techniques to devise an optimal solution for partitioning of a given concurrent task graph, which models the co-design problem, for execution on one software (single CPU) and several hardware resources (multiple FPGA's), with the objective of minimizing the total execution time. Our implementation shows that we can solve problem instances where the task graph has 40 nodes and 600 edges in less than a second.
2011 IEEE International Conference on Automation Science and Engineering, 2011
The combination of fuzzy models could be an ef- fective way to improve system performance. This t... more The combination of fuzzy models could be an ef- fective way to improve system performance. This text proposes a fuzzy approach to the combination of fuzzy models, i.e., the different fuzzy models are combined using a fuzzy rule- based model. The combining fuzzy model is identified using an algorithm that is stable towards disturbances. The combination approach provides simultaneously the
This study considers the problem of Robust Fuzzy approximation of a time-varying nonlinear proces... more This study considers the problem of Robust Fuzzy approximation of a time-varying nonlinear process in the presence of uncertainties in the identification data using a Sugeno Fuzzy System while maintaining the interpretability of the fuzzy model during identification. A recursive procedure for the estimation of fuzzy parameters is proposed based on solving local optimization problem that attempt to minimize the
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2006
This study is concerned with the adaptive learning of an interpretable Sugeno-type fuzzy inferenc... more This study is concerned with the adaptive learning of an interpretable Sugeno-type fuzzy inference system, in a deterministic framework, in the presence of data uncertainties and modeling errors. The authors explore the use of Hinfinity estimation theory and least squares estimation for online learning of membership functions and consequent parameters without making any assumption and requiring a priori knowledge of upper bounds, statistics, and distribution of data uncertainties and modeling errors. The issues of data uncertainties, modeling errors, and time variations have been considered mathematically in a sensible way. The proposed robust approach to the adaptive learning of fuzzy models has been illustrated through the examples of adaptive system identification, time-series prediction, and estimation of an uncertain process.
Mental stress is accompanied by dynamic changes in autonomic nervous system (ANS) activity. Heart... more Mental stress is accompanied by dynamic changes in autonomic nervous system (ANS) activity. Heart rate variability (HRV) analysis is a popular tool for assessing the activities of autonomic nervous system. This paper presents a novel method of HRV analysis for mental stress assessment using fuzzy clustering and robust identification techniques. The approach consists of 1) online monitoring of heart rate
ABSTRACT Stationary fuzzy Fokker-Planck learning (SFFPL) is a recently introduced computational m... more ABSTRACT Stationary fuzzy Fokker-Planck learning (SFFPL) is a recently introduced computational method that applies fuzzy modeling to solve optimization problems. This study develops a concept of applying SFFPL-based computations for nonlinear constrained optimization. We consider the development of SFFPL-based optimization algorithms which do not require derivatives of the objective function and of the constraints. The sequential penalty approach was used to handle the inequality constraints. It was proved under some standard assumptions that the carefully designed SFFPL-based algorithms converge asymptotically to the stationary points. The convergence proofs follow a simple mathematical approach and invoke mean-value theorem. The algorithms were evaluated on the test problems with the number of variables up to 50. The performance comparison of the proposed algorithms with some of the standard optimization algorithms further justifies our approach. The SFFPL-based optimization approach, due to its novelty, could possibly be extended to several research directions.
We believe that nonlinear fuzzy filtering techniques may be turned out to give better robustness ... more We believe that nonlinear fuzzy filtering techniques may be turned out to give better robustness performance than the existing linear methods of estimation (2 and filtering techniques), because of the fact that not only linear parameters (consequents), but also the nonlinear parameters (membership functions) attempt to identify the uncertain behavior of the unknown system. However, the fuzzy identification methods must be robust to data uncertainties and modeling errors to ensure that the fuzzy approximation of unknown system's behavior is optimal in some sense. This study presents a deterministic approach to the robust design of fuzzy models in the presence of unknown but finite uncertainties in the identification data. We consider online identification of an interpretable fuzzy model, based on the robust solution of a regularized least-squares fuzzy parameters estimation problem. The aim is to resolve the difficulties associated with the robust fuzzy identification method due to lack of a priori knowledge about upper bounds on the data uncertainties. The study derives an optimal level of regularization that should be provided to ensure the robustness of fuzzy identification strategy by achieving an upper bound on the value of energy gain from data uncertainties and modeling errors to the estimation errors. A time-domain feedback analysis of the proposed identification approach is carried out with emphasis on stability, robustness, and steady-state issues. The simulation studies are provided to show the superiority of the proposed fuzzy estimation over the classical estimation methods.
This study considers the robust identification of the parameters describing a Sugeno type fuzzy i... more This study considers the robust identification of the parameters describing a Sugeno type fuzzy inference system with uncertain data. The objective is to minimize the worst-case residual error using a numerically efficient algorithm. The Sugeno type fuzzy systems are linear in consequent parameters but nonlinear in antecedent parameters. The robust consequent parameters identification problem can be formulated as second-order cone programming problem. The optimal solution of this second-order cone problem can be interpreted as solution of a Tikhonov regularization problem with a special choice of regularization parameter which is optimal for robustness (Ghaoui and Lebret (1997). SAIM Journal of Matrix Analysis and Applications 18, 1035-1064). The final regularized nonlinear optimization problem allowing simultaneous identification of antecedent and consequent parameters is solved iteratively using a generalized Gauss-Newton like method. To illustrate the approach, several simulation studies on numerical examples including the modelling of a spectral data function (one-dimensional benchmark example) is provided. The proposed robust fuzzy identification scheme has been applied to approximate the physical fitness of patients with a fuzzy expert system. The identified fuzzy expert system is shown to be capable of capturing the decisions (experiences) of a medical expert.
This study presents a new approach to adaptation of Sugeno type fuzzy inference systems using reg... more This study presents a new approach to adaptation of Sugeno type fuzzy inference systems using regularization, since regularization improves the robustness of standard parameter estimation algorithms leading to stable fuzzy approximation. The proposed method can be used for modelling, identification and control of physical processes. A recursive method for on-line identification of fuzzy parameters employing Tikhonov regularization is suggested. The power of approach was shown by applying it to the modelling, identification, and adaptive control problems of dynamic processes. The proposed approach was used for modelling of human-decisions (experience) with a fuzzy inference system and for the fuzzy approximation of physical fitness with real world medical data.
This study presents a method of adaptive identification of parameters describing Sugeno fuzzy inf... more This study presents a method of adaptive identification of parameters describing Sugeno fuzzy inference system in presence of bounded disturbances while maintaining the readability and interpretability of the fuzzy model during and after identification. This method do not require any a priori knowledge of a bound on the disturbance and noise and of a bound on the unknown parameters values. The method can be used for the robust and adaptive identification of slowly time varying nonlinear systems using fuzzy inference systems. The suggested method was used to build a fuzzy expert system that approximates the functional relationship between physical fitness and some of the measurable physiological parameters by their real measurements and opinion (human-experiences) of a medical expert.
This study introduces a fuzzy filtering based technique for rendering robustness to the modelling... more This study introduces a fuzzy filtering based technique for rendering robustness to the modelling methods. We consider a case study dealing with the development of a model for predicting the bioconcentration factor (BCF) of chemicals. The conventional neural/fuzzy BCF models, ...
A novel method for the robust identification of interpretable fuzzy models, based on the criterio... more A novel method for the robust identification of interpretable fuzzy models, based on the criterion that identification errors are least sensitive to data uncertainties and modelling errors, is suggested. The robustness of identification errors towards unknown disturbances (data ...
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Papers by Mohit Kumar