Effective Behavioural Dynamic Coupling through Echo State Networks
نویسندگان
چکیده
منابع مشابه
Synchronization for Complex Dynamic Networks with State and Coupling Time-Delays
This paper is concerned with the problem of synchronization for complex dynamic networks with state and coupling time-delays. Therefore, larger class and more complicated complex dynamic networks can be considered for the synchronization problem. Based on the Lyapunov-Krasovskii functional, a delay-independent criterion is obtained and formulated in the form of linear matrix inequalities (LMIs)...
متن کاملRestricted Echo State Networks
Echo state networks are a powerful type of reservoir neural network, but the reservoir is essentially unrestricted in its original formulation. Motivated by limitations in neuromorphic hardware, we remove combinations of the four sources of memory—leaking, loops, cycles, and discrete time—to determine how these influence the suitability of the reservoir. We show that loops and cycles can replic...
متن کاملsynchronization for complex dynamic networks with state and coupling time-delays
this paper is concerned with the problem of synchronization for complex dynamic networks with state and coupling time-delays. therefore, larger class and more complicated complex dynamic networks can be considered for the synchronization problem. based on the lyapunov-krasovskii functional, a delay-independent criterion is obtained and formulated in the form of linear matrix inequalities (lmis)...
متن کاملExtending Stability Through Hierarchical Clusters in Echo State Networks
Echo State Networks (ESN) are reservoir networks that satisfy well-established criteria for stability when constructed as feedforward networks. Recent evidence suggests that stability criteria are altered in the presence of reservoir substructures, such as clusters. Understanding how the reservoir architecture affects stability is thus important for the appropriate design of any ESN. To quantit...
متن کاملTimeWarping Invariant Echo State Networks
Echo State Networks (ESNs) is a recent simple and powerful approach to training recurrent neural networks (RNNs). In this report we present a modification of ESNs-time warping invariant echo state networks (TWIESNs) that can effectively deal with time warping in dynamic pattern recognition. The standard approach to classify time warped input signals is to align them to candidate prototype patte...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Applied Sciences
سال: 2019
ISSN: 2076-3417
DOI: 10.3390/app9071300