It investigates systematically the reservoir construction of Echo State Network (ESN). This thesis proposes two very simple deterministic ESN organisation (Simple Cycle reservoir (SCR)) and Cycle Reservoir with Jumps (CRJ). Simple Cycle reservoir (SCR) is sufficient to obtain performances comparable to those of the classical ESN. While Cycle Reservoir with Jumps (CRJ) significantly outperform the those of the classical ESN. This thesis also studies and discusses three reservoir characterisations - short-term memory capacity (MC), eigen-spectrum of the reservoir weight matrix and Lyapunov Exponent with their relation to the ESN performance. It also designs and utilises an ensemble of ESNs with diverse reservoirs whose collective readout is obtained through Negative Correlation Learning (NCL) of ensemble of Multi-Layer Perceptrons (MLP), where each individual MPL realises the readout from a single ESN. Finally, this thesis investigates the relation between two quantitative measures characterising short term memory in input driven dynamical systems, namely the short term memory capacity (MC), and the Fisher memory curve (FMC).