Quantum Neural Networks is one of the new concept which exploits the advantages of both traditional neural networks and concept of quantum computation. A simple hybrid three layer quantum neural networks model is studied, which is composed of both quantum and traditional neurons. The weight of the quantum neuron is quantum bits and its transforming function is an inner product operator. The inp...

Railway scheduling is a complex task of rail operators that involves the generation of a conflict-free train timetable. This paper presents a discrete-event simulation-based optimization approach for solving the train timetabling problem to minimize total weighted unplanned stop time in a hybrid single and double track railway networks. The designed simulation model is used as a platform for ge...

Hybrid ad hoc networks consist of two kinds of nodes, regular nodes and nodes with additional capabilities. For example, multi-hop cellular and wireless Internet networks consist of static or mobile nodes and access points to a fixed infrastructure. Each node may access fixed infrastructure either directly or via other nodes in multi-hop fashion. Another example is heterogeneous sensor networks...

Hybrid wireless networks are a viable networking solution to combat the limitations of infrastructured wireless networks and provide Internet connectivity to ad hoc networks. This paper first analyzes the requirements for deployment of hybrid networks under different application scenarios. Then two routing schemes designed for different traffic patterns in hybrid networks are proposed to achiev...

The advanced wireless sensor networks constitute one of the promising application areas of the recently developed wireless sensor networking techniques. It contains two parts: the terrestrial wireless sensor networks (WSN) and the underground wireless sensor networks (WUSN). The main difference between the hybrid WSN and the conventional wireless sensor networks is the aspect of WUSN, which com...

We use a Markov Chain Monte Carlo (MCMC) MML algorithm to learn hybrid Bayesian networks from observational data. Hybrid networks represent local structure, using conditional probability tables (CPT), logit models, decision trees or hybrid models, i.e., combinations of the three. We compare this method with alternative local structure learning algorithms using the MDL and BDe metrics. Results a...

We present a hybrid architecture of recurrent neural networks (RNNs) inspired by hidden Markov models (HMMs). We train the hybrid architecture using genetic algorithms to learn and represent dynamical systems. We train the hybrid architecture on a set of deterministic finite-state automata strings and observe the generalization performance of the hybrid architecture when presented with a new se...

Hybrid dynamic systems are of major research interest nowadays due to their use as models in many applications in computer science and systems control. One of the major problems in hybrid dynamic systems is establishing their key property of stability, which also is important in controller design. The use of communication networks in supervisory control of distributed control systems introduces...

We describe General Network (GenNet), a software plugin for the real time experimental interface (RTXI) dynamic clamp system that allows for straightforward and flexible implementation of hybrid network experiments. This extension to RTXI allows for hybrid networks that contain an arbitrary number of simulated and real neurons, significantly improving upon previous solutions that were limited, ...

In practical situations, distribution network loads are the mixtures of residential, industrial, and commercial types. This paper presents a hybrid optimization algorithm for the optimal placement of shunt capacitor banks in radial distribution networks in the presence of different voltage-dependent load models. The algorithm is based on the combination of Genetic Algorithm (GA) and Binary Part...