Sensor networks require robust and efficient communication protocols to maximise the network lifetime. Radio irregularity, channel fading and interference results in larger energy consumption and latency for packet transmission over wireless channel. Cooperative multi-input multi-output (MIMO) schemes can combat the fading effects in wireless sensor network (WSN) and can significantly improve t...

Multi-Input Multi-Output (MIMO) transmission technique, in which both transmitter and receiver are equipped with multiple antennas, is attracting attention due to its many advantages such as its throughput improvement. For this reason, various cellular mobile radio systems based on MIMO have been developed such as 3GPP LTE (Long Term Evolution) [1] and Mobile WiMAX [2]. MIMO technique is also a...

In the field of control theory system identification is an important way of investigating and understanding the system. Several distinct approaches of system identification produce the different form of knowledge about the system. In system identification, neural network plays an important role. A novel technique has been considered to identify the nonlinear multi input multi output (MIMO) cros...

In this paper, robust adaptive neural network control is investigated for a class of multi-input-multi-output (MIMO) pure-feedback nonlinear system with unknown nonlinearities. The unknown nonlinearities could be come from unmodeled dynamics, modeling errors, or nonlinear time-varying uncertainties. Based on the backstepping design technique and the universal approximation property of the neura...

Control system design for non-affine systems is a difficult problem because of the lack of mathematical tools. The key to the problem is solving for an implicit function but the known results for implicit functions are not applicable for higher dimensional systems except for single-input and single-output systems. In this paper, a new version of a global implicit function theorem in higher dime...

Complex dynamic behavior of nonlinear structures makes it challenging for uncertainty analysis through Monte Carlo simulations (MCS). Surrogate modeling presents an efficient and accurate computational alternative a large number MCS. The previous study has demonstrated that the multi-input multi-output autoregressive with exogenous input (MIMO-NARX) model provides good discrete-time representat...

An adaptive fuzzy neural networks (FNN) output feedback control approach is proposed for a class of multi-input and multi-output (MIMO) pure-feedback nonlinear systems with unknown backlash-like hysteresis and immeasurable states. The state observers are designed to estimate the unmeasured states, the filtered signals are introduced to circumvent algebraic loop problem encountered in the implem...

At present, almost all model order reduction methods assume single-input single-output (SISO) systems or systems with a small number of inputs and outputs. Few methods can deal with systems with a large number of inputs and outputs. Multi-input multi-output (MIMO) systems appear for example in modeling of integrated circuits. The number of inputs and outputs sometimes is very large, even close ...

Space-time block codes (STBC) and spatial multiplexing (SM) are promising techniques that effectively exploit multipleinput multiple-output (MIMO) transmission to achieve more reliable communication and a higher multiplexing rate, respectively. In this paper, we study a practical design for hybrid scheme with multi-input multi-output orthogonal frequency division multiplexing (MIMOOFDM) systems...