Luís F. Mendonça

Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients

Abstract This paper proposes a modified binary particle swarm optimization (MBPSO) method for feature selection with the simultaneous optimization of SVM kernel parameter setting, applied to mortality prediction in septic patients. An enhanced version of binary particle swarm optimization, designed to cope with premature convergence of the BPSO algorithm is proposed. MBPSO control the swarm variability using the velocity and the similarity between best swarm solutions. This paper uses support vector machines in a wrapper approach, where the kernel parameters are optimized at the same time. The approach is applied to predict the outcome (survived or deceased) of patients with septic shock. Further, MBPSO is tested in several benchmark datasets and is compared with other PSO based algorithms and genetic algorithms (GA). The experimental results showed that the proposed approach can correctly select the discriminating input features and also achieve high classification accuracy, specially when compared to other PSO based algorithms. When compared to GA, MBPSO is similar in terms of accuracy, but the subset solutions have less selected features.

Decision tree search methods in fuzzy modeling and classification

This paper proposes input selection methods for fuzzy modeling, which are based on decision tree search approaches. The branching decision at each node of the tree is made based on the accuracy of the model available at the node. We propose two different approaches of decision tree search algorithms: bottom-up and top-down and four different measures for selecting the most appropriate set of inputs at every branching node (or decision node). Both decision tree approaches are tested using real-world application examples. These methods are applied to fuzzy modeling of two different classification problems and to fuzzy modeling of two dynamic processes. The models accuracy of the four different examples are compared in terms of several performance measures. Moreover, the advantages and drawbacks of using bottom-up or top-down approaches are discussed.

An architecture for fault detection and isolation based on fuzzy methods

Model-based fault detection and isolation (FDI) is an approach with increasing attention in the academic and industrial fields, due to economical and safety related matters. In FDI, the discrepancies between system outputs and model outputs are called residuals, and are used to detect and isolate faults. This paper proposes a model-based architecture for fault detection and isolation based on fuzzy methods. Fuzzy modeling is used to derive nonlinear models for the process running in normal operation and for each fault. When a fault occurs, fault detection is performed using the residuals. Then, the faulty fuzzy models are used to isolate a fault. The FDI architecture proposed in this paper uses a fuzzy decision making approach to isolate faults, which is based on the analysis of the residuals. Fuzzy decision factors are derived to isolate faults. An industrial valve simulator is used to obtain several abrupt and incipient faults, which are some of the possible faults in the real system. The proposed fuzzy FDI architecture was able to detect and isolate the simulated abrupt and incipient faults.

Optimization problems in multivariable fuzzy predictive control

Abstract The application of model predictive control (MPC) to complex, nonlinear processes results in a non-convex optimization problem for computing the optimal control actions. This optimization problem can be solved by discrete search techniques such as the branch-and-bound method (B&B), which has been successfully applied to MPC. However, the discretization induced by B&B introduces a tradeoff between the number of discrete actions and the performance. This paper proposes a solution for non-convex optimization problems in multiple-input multiple-output (MIMO) systems. Fuzzy predictive filters, which are represented as an adaptive set of control actions multiplied by gain factors, are extended for MIMO systems. This solution keeps the number of necessary alternatives low and increases the performance. The proposed MPC method using fuzzy predictive filters is applied to the control of a gantry crane. Simulation results show the advantages of the proposed method.

Uncalibrated Eye-to-Hand Visual Servoing Using Inverse Fuzzy Models

A new uncalibrated eye-to-hand visual servoing based on inverse fuzzy modeling is proposed in this paper. In classical visual servoing, the Jacobian plays a decisive role in the convergence of the controller, as its analytical model depends on the selected image features. This Jacobian must also be inverted online. Fuzzy modeling is applied to obtain an inverse model of the mapping between image feature variations and joint velocities. This approach is independent from the robots kinematic model or camera calibration and also avoids the necessity of inverting the Jacobian online. An inverse model is identified for the robot workspace, using measurement data of a robotic manipulator. This inverse model is directly used as a controller. The inverse fuzzy control scheme is applied to a robotic manipulator performing visual servoing for random positioning in the robot workspace. The obtained experimental results show the effectiveness of the proposed control scheme. The fuzzy controller can position the robotic manipulator at any point in the workspace with better accuracy than the classic visual servoing approach.

FAULT ISOLATION USING FUZZY MODEL-BASED OBSERVERS

Abstract Model-based fault detection and isolation is an approach with increasing attention in the academic and industrial fields, due to economical and safety related matters. However, in practice it is very difficult to achieve accurate models for complex nonlinear plants. The inherent characteristics of fuzzy logic theory makes it suitable for Fault Detection and Isolation (FDI). This paper proposes a regularity criterion to determine the structure of fuzzy models and a fuzzy decision making approach to isolate faults. An industrial valve simulator is used to detect and isolate several abrupt and incipient faults.

Fault tolerant control using a fuzzy predictive approach

This paper proposes the application of fault-tolerant control (FTC) using fuzzy predictive control. The FTC approach is based on two steps, fault detection and isolation (FDI) and fault accommodation. The fault detection is performed by a model-based approach using fuzzy modeling and fault isolation uses a fuzzy decision making approach. The information obtained on the FDI step is used to select the model to be used in fault accommodation, in a model predictive control (MPC) scheme. The fault accommodation is performed with one fuzzy model for each identified fault. The FTC scheme is used to accommodate the faults of two systems a container gantry crane and three tank benchmark system. The fuzzy FTC scheme proposed in this paper was able to detect, isolate and accommodate correctly the considered faults of both systems.

Metaheuristics for feature selection: Application to sepsis outcome prediction

This paper proposes the application of a new binary particle swarm optimization (BPSO) method to feature selection problems. Two enhanced versions of binary particle swarm optimization, designed to cope with premature convergence of the BPSO algorithm, are proposed. These methods control the swarm variability using the velocity and the similarity between best swarm solutions. The proposed PSO methods use neural networks, fuzzy models and support vector machines in a wrapper approach, and are tested in a benchmark database. It was shown that the proposed BPSO approaches require an inferior simulation time, less selected features and increase accuracy. The best BPSO is then compared with genetic algorithms (GA) and applied to a real medical application, a sepsis patient database. The objective is to predict the outcome (survived or deceased) of the sepsis patients. It was shown that the proposed BPSO approaches are similar in terms of model accuracy when compared to GA, while requiring an inferior simulation time and less selected features.

Application of evolving fuzzy modeling to fault tolerant control

This paper proposes the application of evolving fuzzy modeling to fault-tolerant in two steps: fault detection and fault accommodation. Fault accommodation uses evolving Takagi–Sugeno fuzzy models, and fault detection uses a model-based approach also based on fuzzy models. Information from fault detection is used for fault accommodation in a model predictive control (MPC) scheme. The evolving fuzzy modeling approach increases the control performance when the process is with faults. The proposed approach continuously evaluate the control performance and perform on-line clustering, when necessary. Evolving FTC is used to accommodate two simulated faults in a distillation column process. The considered faults are the load process fault (variation in feed composition) and the change in heating (variation of re-boiler temperature). The fault tolerant control using evolving fuzzy modeling was able to accommodate the simulated faults.

Fuzzy model-based fault detection and isolation

The model-based fault detection and diagnosis (FDI) is an interesting method, because of economical and safety related matters. However, in practice it is very difficult to achieve an accurate modeling for complex nonlinear systems. If the system structure is not precisely known, the diagnosis has to be based primarily on data or heuristic information. Fuzzy system theory is an interesting tool to handle these situations. The use of characteristics of fuzzy logic theory makes it suitable for fault diagnosis. In this paper, a simulator of an industrial servo-actuated valve is used to simulate several faults in the system. These faults are some of the possible faults in the real system. The fuzzy model-based FDI system developed in this paper was able to detect and isolate all the simulated faults.