W M Hinojosa

Bio: W M Hinojosa is an academic researcher from University of Salford. The author has contributed to research in topics: Probability distribution & Fuzzy control system. The author has an hindex of 1, co-authored 1 publications receiving 30 citations.

Systems Control With Generalized Probabilistic Fuzzy-Reinforcement Learning

Abstract: Reinforcement learning (RL) is a valuable learning method when the systems require a selection of control actions whose consequences emerge over long periods for which input-output data are not available. In most combinations of fuzzy systems and RL, the environment is considered to be deterministic. In many problems, however, the consequence of an action may be uncertain or stochastic in nature. In this paper, we propose a novel RL approach to combine the universal-function-approximation capability of fuzzy systems with consideration of probability distributions over possible consequences of an action. The proposed generalized probabilistic fuzzy RL (GPFRL) method is a modified version of the actor-critic (AC) learning architecture. The learning is enhanced by the introduction of a probability measure into the learning structure, where an incremental gradient-descent weight-updating algorithm provides convergence. Our results show that the proposed approach is robust under probabilistic uncertainty while also having an enhanced learning speed and good overall performance.

A Decentralized Fuzzy Learning Algorithm for Pursuit-Evasion Differential Games with Superior Evaders

Abstract: In this paper, we consider multi-pursuer single-superior-evader pursuit-evasion differential games where the evader has a speed that is similar to or higher than the speed of each pursuer. A new fuzzy reinforcement learning algorithm is proposed in this work. The proposed algorithm uses the well-known Apollonius circle mechanism to define the capture region of the learning pursuer based on its location and the location of the superior evader. The proposed algorithm uses the Apollonius circle with a developed formation control approach in the tuning mechanism of the fuzzy logic controller (FLC) of the learning pursuer so that one or some of the learning pursuers can capture the superior evader. The formation control mechanism used by the proposed algorithm guarantees that the pursuers are distributed around the superior evader in order to avoid collision between pursuers. The formation control mechanism used by the proposed algorithm also makes the Apollonius circles of each two adjacent pursuers intersect or be at least tangent to each other so that the capture of the superior evader can occur. The proposed algorithm is a decentralized algorithm as no communication among the pursuers is required. The only information the proposed algorithm requires is the position and the speed of the superior evader. The proposed algorithm is used to learn different multi-pursuer single-superior-evader pursuit-evasion differential games. The simulation results show the effectiveness of the proposed algorithm.

Conditional Density Estimation Using Probabilistic Fuzzy Systems

Abstract: We consider conditional density approximation by fuzzy systems. Fuzzy systems are typically used to approximate deterministic functions in which the stochastic uncertainty is ignored. We propose probabilistic fuzzy systems (PFSs), in which the probabilistic nature of uncertainty is taken into account. These systems take also fuzzy uncertainty into account by their fuzzy partitioning of input and output spaces. We discuss an additive reasoning scheme for PFSs that leads to the estimation of conditional probability densities and prove how such fuzzy systems compute the expected value of this conditional density function. We show that some of the most commonly used fuzzy systems can compute the same expected output value, and we derive how their parameters should be selected in order to achieve this goal. The additional information and process understanding provided by the different interpretations of the PFS models are illustrated using a real-world example.

Hesitant probabilistic fuzzy set based time series forecasting method

Abstract: Uncertainties due to randomness and fuzziness coexist in the system simultaneously. Recently probabilistic fuzzy set has gained attention of researchers to handle both types of uncertainties simultaneously in a single framework. In this paper, we introduce hesitant probabilistic fuzzy sets in time series forecasting to address the issues of non-stochastic non-determinism along with both types of uncertainties and propose a hesitant probabilistic fuzzy set based time series forecasting method. We also propose an aggregation operator that uses membership grades, weights and immediate probability to aggregate hesitant probabilistic fuzzy elements to fuzzy elements. Advantages of the proposed forecasting method are that it includes both type of uncertainties and non-stochastic hesitation in a single framework and also enhance the accuracy in forecasted outputs. The proposed method has been implemented to forecast the historical enrolment student’s data at University of Alabama and share market prizes of State Bank of India (SBI) at Bombay stock exchange (BSE), India. The effectiveness of the proposed method has been examined and tested using error measures.

Control Design of Uncertain Quantum Systems With Fuzzy Estimators

Abstract: An approach of control design using fuzzy estimators (FEs) is proposed for quantum systems with uncertainties. Two types of quantum control problems are considered: 1) control of a pure-state quantum system in the presence of uncertainties and 2) control design of quantum systems with initial mixed states and uncertainties. For the first type of tasks, a partial feedback control scheme with an FE is presented to design controllers. In this scheme, an FE is trained to estimate the quantum state for feedback control of a quantum system, and controlled projective measurement is used to assist in controlling the system. For the second type of quantum control tasks, a probabilistic fuzzy estimator (PFE) is trained to estimate the quantum state for control design of a quantum system with an initial mixed state, and a corresponding control algorithm is proposed to design a control law that drives the system from the mixed state to a target pure state. Two examples of two-spin-1/2 systems are also presented and analyzed to demonstrate the process of control design and potential applications of the proposed approach.

Posture self-stabilizer of a biped robot based on training platform and reinforcement learning

Abstract: In order to solve the problem of stability control for biped robots, the concept of stability training is proposed by using a training platform to exert random disturbance with amplitude limitation on robots that are to be trained. In this work, an approach to achieve a posture stabilizing capability based on stability training and reinforcement learning is explored and verified by simulations. An automatic abstraction method for state space is proposed by using the Gauss basis function and inner evaluation indexes to speed up the learning process. Hierarchical structure stabilizer using the Monte Carlo method is designed according to the concept of variable ZMP. Training samples are extracted from the state transition of the stability training process using balance controllers based on the robot dynamic model. The stabilizers are trained with and without applying the automatic abstraction of state space. Then simulation tests of them are conducted under conditions where the training platform exerts amplitude-limited random disturbances on the robot. Also, the influence of the model errors is studied by introducing deviations of the CoM position during the simulation tests. By comparing the simulation results of two learning stabilizers and the model-based balance controller, it is demonstrated that the designed stabilizer can achieve approximate success rate of the ideal model-based balance controller and exert all the driving ability of the robot under the large disturbance condition of 30 inclination of the platform. Also, the effects of the model error can be overcome by retraining using state transition data with the model error. The active training concept is proposed by applying a training platform.The training platform disturbs robots on it with amplitude-limited random motions.An automatic abstraction method is proposed for the high-dimensional state space.A learning posture stabilizer with hierarchical structure is designed.