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Showing 1 to 11 of 11 for “"echo state network"”.

  1. Two-Phase Buck Converter Optimize by Echo State Network

    … efficiency. The inspiration of the neural network is derived from the biological brain, neural is similar with the human neural, and the synaptic weights can treat as the connection between two nodes. Reservoir computing can be seen as an extension of the neural network since it is a …

    vt Repository record for Two-Phase Buck Converter Optimize by Echo State Network (opens in a new tab)

  2. Biologically inspired goal directed navigation for mobile robots

    … and a bio-inspired approach using an Echo State Network and Liquid State Machine architecture was chosen as the base for the navigation modules. The navigation module implemented in this work is trained to navigate and localise itself in different environments drawing its inspiration …

    cape-town Repository record for Biologically inspired goal directed navigation for mobile robots (opens in a new tab)

  3. MIMO-OFDM Symbol Detection via Echo State Networks

    Echo state network (ESN) is a specific neural network structure composed of high dimensional nonlinear dynamics and learned readout weights. This thesis considers applying ESN for symbol detection in multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. A …

    vt Repository record for MIMO-OFDM Symbol Detection via Echo State Networks (opens in a new tab)

  4. A Cost-Efficient Digital ESN Architecture on FPGA

    Echo State Network (ESN) is a recently developed machine-learning paradigm whose processing capabilities rely on the dynamical behavior of recurrent neural networks (RNNs). Its performance metrics outperform traditional RNNs in nonlinear system identification and temporal information processing. In …

    vt Repository record for A Cost-Efficient Digital ESN Architecture on FPGA (opens in a new tab)

  5. Real-time data assimilation in nonlinear dynamical systems

    … the methodology to infer the thermoacoustic states and heat release parameters on the fly without storing data (real-time). We perform twin experiments using synthetic acoustic pressure observations to analyse the performance of data assimilation in all nonlinear thermoacoustic regimes, from …

    cambridge Repository record for Real-time data assimilation in nonlinear dynamical systems (opens in a new tab)

  6. Deep Reinforcement Learning for Next Generation Wireless Networks with Echo State Networks

    … environments is to combine recurrent neural network (RNN) and DRL to capture temporal information inherent in the system, which is referred to as deep recurrent Q-network (DRQN). However, training DRQN is known to be challenging requiring a large amount of training data to achieve …

    vt Repository record for Deep Reinforcement Learning for Next Generation Wireless Networks with Echo State Networks (opens in a new tab)

  7. FINITE ELEMENT METHODS AND MACHINE LEARNING FOR SOME MULTI-PHASE PROBLEMS

    … model is examined. The deep learning methods are echo state network (ESN) and long short-term mem- ory (LSTM). The numerical discretization scheme of the Cahn-Hilliard system is briefly discussed. Then we present architectures of the ESN and the LSTM. We show that LSTM substantially out- performs …

    houston Repository record for FINITE ELEMENT METHODS AND MACHINE LEARNING FOR SOME MULTI-PHASE PROBLEMS (opens in a new tab)

  8. On-device mobile speech recognition

    … part of this thesis presents two novel Neural Network approaches to mobile Speech recognition. Firstly, a recurrent neural networks architecture is developed to accommodate the output of the VAD stage. Specifically, an Echo State Network (ESN) is used for phoneme level recognition. The …

    nott-trent Repository record for On-device mobile speech recognition (opens in a new tab)

  9. Neural networks for the prediction of chaos and turbulence

    … spatiotemporal chaos. The methods are based on echo state networks (ESNs), which are versatile recurrent neural networks that learn temporal correlations within data. In the first part of the thesis, we focus on the network's hyperparameters, which markedly affect the performance of the machine. …

    cambridge Repository record for Neural networks for the prediction of chaos and turbulence (opens in a new tab)

  10. Data-Driven Physical-Layer Optimization for RF and Optical Communications: Transmitarray Metasurfaces, Contextual-Bandit MUD, and Coherent Optical Links

    … or centric ring), which together cover the 36-state, 10-degree-quantized phase library used for beam forming. Characterizing every pattern in HFSS is infeasible, so a one-dimensional convolutional network with cross-attention is trained to predict the scattering parameters from the binary pixel …

    embry-riddle Repository record for Data-Driven Physical-Layer Optimization for RF and Optical Communications: Transmitarray Metasurfaces, Contextual-Bandit MUD, and Coherent Optical Links (opens in a new tab)

  11. Deep neural network acoustic models for multi-dialect Arabic speech recognition

    … time varying signals. Artificial Neural Networks (ANNs) have also been widely used for representing time varying quasi-stationary signals. Arabic is one of the oldest living languages and one of the oldest Semitic languages in the world, it is also the fifth most generally used language …

    nott-trent Repository record for Deep neural network acoustic models for multi-dialect Arabic speech recognition (opens in a new tab)