Identification of Nonlinear Systems From the Knowledge Around Different Operating Conditions: A Feed-Forward Multi-Layer ANN Based Approach

The paper investigates nonlinear system identification using system output data at various linearized operating points. A feed-forward multi-layer Artificial Neural Network (ANN) based approach is used for this purpose and tested for two target applications i.e. nuclear reactor power level monitoring and an AC servo position control system. Various configurations of ANN using different activation functions, number of hidden layers and neurons in each layer are trained and tested to find out the best configuration. The training is carried out multiple times to check for consistency and the mean and standard deviation of the root mean square errors (RMSE) are reported for each configuration.Comment: "6 pages, 9 figures; The Second IEEE International Conference on Parallel, Distributed and Grid Computing (PDGC-2012), December 2012, Solan

Strategies for neural networks in ballistocardiography with a view towards hardware implementation

A thesis submitted for the degree of Doctor of Philosophy at the University of LutonThe work described in this thesis is based on the results of a clinical trial conducted by the research team at the Medical Informatics Unit of the University of Cambridge, which show that the Ballistocardiogram (BCG) has prognostic value in detecting impaired left ventricular function before it becomes clinically overt as myocardial infarction leading to sudden death. The objective of this study is to develop and demonstrate a framework for realising an on-line BCG signal classification model in a portable device that would have the potential to find pathological signs as early as possible for home health care. Two new on-line automatic BeG classification models for time domain BeG classification are proposed. Both systems are based on a two stage process: input feature extraction followed by a neural classifier. One system uses a principal component analysis neural network, and the other a discrete wavelet transform, to reduce the input dimensionality. Results of the classification, dimensionality reduction, and comparison are presented. It is indicated that the combined wavelet transform and MLP system has a more reliable performance than the combined neural networks system, in situations where the data available to determine the network parameters is limited. Moreover, the wavelet transfonn requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced. Overall, a methodology for realising an automatic BeG classification system for a portable instrument is presented. A fully paralJel neural network design for a low cost platform using field programmable gate arrays (Xilinx's XC4000 series) is explored. This addresses the potential speed requirements in the biomedical signal processing field. It also demonstrates a flexible hardware design approach so that an instrument's parameters can be updated as data expands with time. To reduce the hardware design complexity and to increase the system performance, a hybrid learning algorithm using random optimisation and the backpropagation rule is developed to achieve an efficient weight update mechanism in low weight precision learning. The simulation results show that the hybrid learning algorithm is effective in solving the network paralysis problem and the convergence is much faster than by the standard backpropagation rule. The hidden and output layer nodes have been mapped on Xilinx FPGAs with automatic placement and routing tools. The static time analysis results suggests that the proposed network implementation could generate 2.7 billion connections per second performance

Design and simulation of advanced fault tolerant flight control schemes

This research effort describes the design and simulation of a distributed Neural Network (NN) based fault tolerant flight control scheme and the interface of the scheme within a simulation/visualization environment. The goal of the fault tolerant flight control scheme is to recover an aircraft from failures to its sensors or actuators. A commercially available simulation package, Aviator Visual Design Simulator (AVDS), was used for the purpose of simulation and visualization of the aircraft dynamics and the performance of the control schemes.;For the purpose of the sensor failure detection, identification and accommodation (SFDIA) task, it is assumed that the pitch, roll and yaw rate gyros onboard are without physical redundancy. The task is accomplished through the use of a Main Neural Network (MNN) and a set of three De-Centralized Neural Networks (DNNs), providing analytical redundancy for the pitch, roll and yaw gyros. The purpose of the MNN is to detect a sensor failure while the purpose of the DNNs is to identify the failed sensor and then to provide failure accommodation. The actuator failure detection, identification and accommodation (AFDIA) scheme also features the MNN, for detection of actuator failures, along with three Neural Network Controllers (NNCs) for providing the compensating control surface deflections to neutralize the failure induced pitching, rolling and yawing moments. All NNs continue to train on-line, in addition to an offline trained baseline network structure, using the Extended Back-Propagation Algorithm (EBPA), with the flight data provided by the AVDS simulation package.;The above mentioned adaptive flight control schemes have been traditionally implemented sequentially on a single computer. This research addresses the implementation of these fault tolerant flight control schemes on parallel and distributed computer architectures, using Berkeley Software Distribution (BSD) sockets and Message Passing Interface (MPI) for inter-process communication

Learning feedforward controller for a mobile robot vehicle

This paper describes the design and realisation of an on-line learning posetracking controller for a three-wheeled mobile robot vehicle. The controller consists of two components. The first is a constant-gain feedback component, designed on the basis of a second-order model. The second is a learning feedforward component, containing a single-layer neural network, that generates a control contribution on the basis of the desired trajectory of the vehicle. The neural network uses B-spline basis functions, enabling a computationally fast implementation and fast learning. The resulting control system is able to correct for errors due to parameter mismatches and classes of structural errors in the model used for the controller design. After sufficient learning, an existing static gain controller designed on the basis of an extensive model has been outperformed in terms of tracking accuracy

Investigation on Mlp Artificial Neural Network Using FPGA For Autonomous Cart Follower System

Dengan kos alat pengesan yang semakin rendah, masa depan sistem pedati pengikut autonomi akan dilengkapi dengan lebih banyak alat pengesan. Ini menjadi cabaran rekabentuk dalam mengendalikan data besar dan kerumitan perkukuhan. Kebanyakan sistem yang sedia ada menggunakan papan mikropengawal yang mempunyai prestasi yang terhad dan pengembangan tidak mungkin tanpa penggantian yang lebih baru. Projek ini mencadangkan perlaksanaan alternatif sistem pedati pengikut autonomi dengan model rangkaian neural MLP menggunakan FPGA. Sistem pedati pengikut autonomi yang mengguakan papan mikropengawal telah diubah suai untuk menggunakan papan FPGA dan dilaksanakan melalui Sistem pada Chip (SOC). System rangkaian neural dilatih dalam simulasi dengan vektor latihan yang dikumpul daripada sistem pedati pengikut autonomi yang sedia ada. System rangkaian neural kemudian dilaksanakan sebagai perkukuhan dalam SOC itu. Dalam pemerhatian, jejak perkukuhan model rangkaian neural kekal saiz kecil tanpa mengira saiz rangkaian neural. Hasil kajian menunjukkan bahawa dengan penggunaan sumber tambahan sebanyak 40%, penambahbaikan sistem secara keseluruhan sebanyak 27 kali dicapai dengan penggunaan blok pecutan perkakasan di SOC, berbanding dengan SOC tanpa penggunaan blok pecutan perkakasan. ________________________________________________________________________________________________________________________ The future of the autonomous cart follower system will equipped with lots of sensory data, due to the ever lower cost of sensory device. This provides design challenge on handling large data and firmware complexity. Most of the existing systems are implemented via usage of microcontroller board, which has limited performance and expansion is not possible without replacement of newer board. The project proposes an alternative approach of running the autonomous cart follower systems on neural network model using Field Programmable Gates Array (FPGA). A microcontroller based autonomous cart follower systems is modified to use the FPGA board and implemented via the System on Chip (SOC) approach. The neural network is trained offline in simulation tools with training vector collected from running the existing autonomous cart follower systems. The trained neural network model then implemented as software code in the SOC. By observation the firmware footprint of the neural network model remains small size regardless of the neural network size. The result shows that with 40% more additional resource utilization, the overall system improvement of 27 times is achieved with the usage of hardware acceleration block in SOC compared to SOC without hardware acceleration

Intrinsically Evolvable Artificial Neural Networks

Dedicated hardware implementations of neural networks promise to provide faster, lower power operation when compared to software implementations executing on processors. Unfortunately, most custom hardware implementations do not support intrinsic training of these networks on-chip. The training is typically done using offline software simulations and the obtained network is synthesized and targeted to the hardware offline. The FPGA design presented here facilitates on-chip intrinsic training of artificial neural networks. Block-based neural networks (BbNN), the type of artificial neural networks implemented here, are grid-based networks neuron blocks. These networks are trained using genetic algorithms to simultaneously optimize the network structure and the internal synaptic parameters. The design supports online structure and parameter updates, and is an intrinsically evolvable BbNN platform supporting functional-level hardware evolution. Functional-level evolvable hardware (EHW) uses evolutionary algorithms to evolve interconnections and internal parameters of functional modules in reconfigurable computing systems such as FPGAs. Functional modules can be any hardware modules such as multipliers, adders, and trigonometric functions. In the implementation presented, the functional module is a neuron block. The designed platform is suitable for applications in dynamic environments, and can be adapted and retrained online. The online training capability has been demonstrated using a case study. A performance characterization model for RC implementations of BbNNs has also been presented

Estimation of physical variables from multichannel remotely sensed imagery using a neural network: Application to rainfall estimation

Satellite-based remotely sensed data have the potential to provide hydrologically relevant information about spatially and temporally varying physical variables. A methodology for estimating such variables from multichannel remotely sensed data is presented; the approach is based on a modified counterpropagation neural network (MCPN) and is both effective and efficient at building complex nonlinear input-output function mappings from large amounts of data. An application to high-resolution estimation of the spatial and temporal variation of surface rainfall using geostationary satellite infrared and visible imagery is presented. Test results also indicate that spatially and temporally sparse ground-based observations can be assimilated via an adaptive implementation of the MCPN method, thereby allowing on-line improvement of the estimates

Autonomously Reconfigurable Artificial Neural Network on a Chip

Artificial neural network (ANN), an established bio-inspired computing paradigm, has proved very effective in a variety of real-world problems and particularly useful for various emerging biomedical applications using specialized ANN hardware. Unfortunately, these ANN-based systems are increasingly vulnerable to both transient and permanent faults due to unrelenting advances in CMOS technology scaling, which sometimes can be catastrophic. The considerable resource and energy consumption and the lack of dynamic adaptability make conventional fault-tolerant techniques unsuitable for future portable medical solutions. Inspired by the self-healing and self-recovery mechanisms of human nervous system, this research seeks to address reliability issues of ANN-based hardware by proposing an Autonomously Reconfigurable Artificial Neural Network (ARANN) architectural framework. Leveraging the homogeneous structural characteristics of neural networks, ARANN is capable of adapting its structures and operations, both algorithmically and microarchitecturally, to react to unexpected neuron failures. Specifically, we propose three key techniques --- Distributed ANN, Decoupled Virtual-to-Physical Neuron Mapping, and Dual-Layer Synchronization --- to achieve cost-effective structural adaptation and ensure accurate system recovery. Moreover, an ARANN-enabled self-optimizing workflow is presented to adaptively explore a "Pareto-optimal" neural network structure for a given application, on the fly. Implemented and demonstrated on a Virtex-5 FPGA, ARANN can cover and adapt 93% chip area (neurons) with less than 1% chip overhead and O(n) reconfiguration latency. A detailed performance analysis has been completed based on various recovery scenarios

To develop an efficient variable speed compressor motor system

This research presents a proposed new method of improving the energy efficiency of a Variable Speed Drive (VSD) for induction motors. The principles of VSD are reviewed with emphasis on the efficiency and power losses associated with the operation of the variable speed compressor motor drive, particularly at low speed operation.The efficiency of induction motor when operated at rated speed and load torque is high. However at low load operation, application of the induction motor at rated flux will cause the iron losses to increase excessively, hence its efficiency will reduce dramatically. To improve this efficiency, it is essential to obtain the flux level that minimizes the total motor losses. This technique is known as an efficiency or energy optimization control method. In practice, typical of the compressor load does not require high dynamic response, therefore improvement of the efficiency optimization control that is proposed in this research is based on scalar control model.In this research, development of a new neural network controller for efficiency optimization control is proposed. The controller is designed to generate both voltage and frequency reference signals imultaneously. To achieve a robust controller from variation of motor parameters, a real-time or on-line learning algorithm based on a second order optimization Levenberg-Marquardt is employed. The simulation of the proposed controller for variable speed compressor is presented. The results obtained clearly show that the efficiency at low speed is significant increased. Besides that the speed of the motor can be maintained. Furthermore, the controller is also robust to the motor parameters variation. The simulation results are also verified by experiment