We consider learning in decentralized heterogeneous networks: agents seek to minimize a convex functional that aggregates data across the network, while only having access their local streams. focus on case where estimate regression function belongs reproducing kernel Hilbert space (RKHS). To incentivize coordination respecting network heterogeneity, we impose nonlinear proximity constraints. T...

Modeling of stream flow–suspended sediment relationship is one of the most studied topics in hydrology due to itsessential application to water resources management. Recently, artificial intelligence has gained much popularity owing toits application in calibrating the nonlinear relationships inherent in the stream flow–suspended sediment relationship. Thisstudy made us of adaptive neuro-fuzzy ...

The stability of learning rate in neural network identifiers and controllers is one of the challenging issues which attracts great interest from researchers of neural networks. This paper suggests adaptive gradient descent algorithm with stable learning laws for modified dynamic neural network (MDNN) and studies the stability of this algorithm. Also, stable learning algorithm for parameters of ...

Wireless Multimedia Sensor Networks (WMSNs) undergo congestion when a link (or a node) becomes overpopulated in terms of incoming packets. In WMSNs this happens especially in upstream nodes where all incoming packets meet and directed to the sink node. Congestion in networks, if not handled properly, might lead to congestion collapse which deteriorates the quality of service (QoS). Therefore, i...