In this paper we propose modifications for the learning rules of Marshall’s EXIN (excitatory + inhibitory) neural network model in order to decrease its computational complexity and understand the role of the weight updating learning rules in correctly encoding familiar, superimposed and ambiguous input patterns. The MEXIN (Modified EXIN) models introduce mixtures of competitive and Hebbian upd...

Distinct explicit and implicit memory processes support weight predictions used when lifting objects and making perceptual judgments about weight, respectively. The first time that an object is encountered weight is predicted on the basis of learned associations, or priors, linking size and material to weight. A fundamental question is whether the brain maintains a single, global representation...

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This paper proposes a modified particle swarm optimization algorithm with dynamic adaptation. In this algorithm, a modified velocity updating formula of the particle is used, where the randomness in the course of updating particle velocity is relatively decreased and the inertia weight of each particle is different. Moreover, this algorithm introduces two parameter describing the evolving state...

The four dimensional pure compact U(1) gauge theory is known to posses a phase transition (PT) separating a confined phase and a Coulomb one and the determination of the order of PT is of great importance. In the pure compact U(1) gauge theory, however, the determination of the order of PT is very difficult since on large lattices the standard updating algorithms like Metropolis, heat-bath, hyb...

This paper presents an efficient method for updating the structural finite element model. Model updating is performed through minimizing the difference of recorded acceleration of real damaged structure and hypothetical damaged structure, by updating physical parameters in each phase using iterative process of Levenberg-Marquardt algorithm. This algorithm is based on sensitivity analysis and pr...

When lifting an object, individuals scale lifting forces based on long-term priors relating external object properties (such as material and size) to object weight. When experiencing objects that are poorly predicted by priors, people rapidly form and update sensorimotor memories that can be used to predict an object's atypical size-weight relation in support of predictively scaling lift forces...

Future frame prediction in videos is a challenging problem because include complicated movements and large appearance changes. Learning-based future approaches have been proposed kinds of literature. A common limitation the existing learning-based mismatch training data test data. In task, we can obtain ground truth by just waiting for few frames. It means update model online phase. Then, propo...

Existing edge computing architectures do not support the updating of neural network models, nor are they optimized for storing, updating, and transmitting different models to a large number IoT devices. In this paper, cloud-edge smart architecture speeding up deployment with transfer learning techniques is proposed. A new model update mechanism based on share weight characteristic proposed addr...