Quantum mechanics

Control (management)

Convex combination

Ellipsoid

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Markov process

Applied mathematics

Set (abstract data type)

Computer science

Mathematical optimization

Bounded function

Physics

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Mathematical analysis

Artificial intelligence

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Jump

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Artificial neural network

Convex optimization

Regular polygon

IEEE Transactions on Neural Networks and Learning Systems

This brief investigates the reachable set estimation problem of the delayed Markovian jump neural networks (NNs) with bounded disturbances. First, an improved reciprocally convex inequality is proposed, which contains some existing ones as its special cases. Second, an augmented Lyapunov-Krasovskii functional (LKF) tailored for delayed Markovian jump NNs is proposed. Thirdly, based on the proposed reciprocally convex inequality and the augmented LKF, an accurate ellipsoidal description of the reachable set for delayed Markovian jump NNs is obtained. Finally, simulation results are given to illustrate the effectiveness of the proposed method.

Reachable Set Estimation of Delayed Markovian Jump Neural Networks Based on an Improved Reciprocally Convex Inequality