Neural networks : a classroom approach

Neural networks with a good modeling capability have been used increasingly to predict and forecast solar radiation. Even diverse application of neural network has been reported in literatures such as robotics, pattern recognition, forecasting, power systems, optimization and social/psychological sciences etc. The models have categorized the review under three major performance schemes such as delay, number of neurons and activation function for establishment of neural network architecture. In each of these categories, we summarize the major applications of eight well recognized and often used neural network models of which the last two are custom based. The anticipated model are initiated and validated with 10 metrological parameters further in sub-categories. Evaluation of its accuracy associated with special flexibility of the model is demonstrated through the results based on parameter range. In summary, we conclude the best result showing that the delays, neuron, transfer function, model, parameters and RMSE errors are in range of 15 or 30, 10 or 20, tansig, Elman Back Propagation network, bulb point temperature or direct normal radiation, 9–10 and 25–35% training to the test cases. The review discloses the incredible view of using the neural networks in solar forecast. The work of other researchers in the ﬁeld of renewable energy and other energy systems is also reported which can be used in the future in the works of this field.

Solar radiation forecasting with multiple parameters neural networks

The low cost inertial navigation system (INS) suffers from bias and measurement noise, which would result in poor navigation accuracy during global positioning system (GPS) outages. Aiming to bridge GPS outages duration and enhance the navigation performance, a deep learning network architecture named GPS/INS neural network (GI-NN) is proposed in this paper to assist the INS. The GI-NN combines a convolutional neural network and a gated recurrent unit neural network to extract spatial features from inertial measurement unit (IMU) signals and track their temporal characteristics. The relationship among the attitude, specific force, angular rate and the GPS position increment is modelled, while the current and previous IMU data are used to estimate the dynamics of the vehicle by GI-NN. Numerical simulations, real field tests and public data tests are performed to evaluate the effectiveness of the proposed algorithm. Compared with the traditional machine learning algorithms, the results illustrate that the proposed method can provide more accurate and reliable navigation solutions in GPS denied environments.

Deep Learning-Enabled Fusion to Bridge GPS Outages for INS/GPS Integrated Navigation

The combined navigation system consisting of Global Positioning System (GPS) and Inertial Navigation System in a complementary mode assures an accurate, reliable, and continuous positioning capability in the navigation system. Because of problems such as dealing with a low-cost MEMS-based inertial sensors having a high level of uncertainty and imprecision, stochastic noise, a high-speed vehicle, high noisy real data, and long-term GPS signal outage during the real-time flight test, the advantage is taken for some approaches in different steps: (1) utilizing discrete wavelet transform technique to enhance the signal-to-noise ratio in raw and noisy inertial sensor signals and attenuate high-frequency noise as a preprocessing phase to prepare more accurate data for the proposed model and (2) employing adaptive neural subtractive clustering fuzzy inference system (ANSCFIS) which combines and extracts the best feature of adaptive neuro-fuzzy inference system (ANFIS), and the subtractive clustering algorithm with fewer rules than the ANFIS method, aiming to improve a more efficient, accurate, and especially a faster method which enhances the prediction accuracy and speeds up the positioning system. The achieved accuracies for the proposed model are discussed and compared with the extended Kalman filter (EKF), ANFIS, and ANSCFIS which are implemented and tested experimentally using a high-speed vehicle in three GPS blockages. The proposed model shows considerable improvements in high-speed navigation using low-cost MEMS-based inertial sensors in case of long-term GPS blockage.

Wavelet-adaptive neural subtractive clustering fuzzy inference system to enhance low-cost and high-speed INS/GPS navigation system

WhONet: Wheel Odometry neural Network for vehicular localisation in GNSS-deprived environments

An aircraft system mainly relies on a Global Positioning System GPS to provide accurate position values consistently. However, GPS receivers may encounter frequent GPS absence because of ephemeric error, satellite clock error, multipath error, and signal jamming. To overcome these drawbacks, generally a GPS is integrated with an Inertial Navigation System INS mounted inside the vehicle to provide a reliable navigation solution. INS and GPS are commonly integrated using a Kalman filter KF to provide a robust navigation solution. In the KF approach, the error models of both INS and GPS are required; this leads to the complexity of the system. This research work presents new position update architecture NPUA which consists of various artificial intelligence neural networks AINN that integrate both GPS and INS to overcome the drawbacks of the Kalman filter. The various AINNs that include both static and dynamic networks described for the system are radial basis function neural network RBFNN, backpropagation neural network BPN, forward-only counter propagation neural network FCPN, full counter propagation neural network Full CPN, adaptive resonance theory-counter propagation neural network ART-CPN, constructive neural network CNN, higher-order neural networks HONN, and input-delayed neural networks IDNN to predict the INS position error during GPS absence, resulting in different performances. The performances of the different AINNs are analyzed in terms of root mean square error RMSE, performance index PI, number of epochs, and execution time ET.

Performance analysis of various artificial intelligent neural networks for gps/ins integration

In this paper, efficient charging of electric vehicle battery from a considered renewable solar photovoltaic source with the help of a modified Z source with efficient boosting topology. Adapting this Z-source converter to act as a voltage gainer with a boosting function allows a solar Photovoltaic (PV) input voltage of 25VDC (Volts Direct Current) to be increased to a designed output voltage of 75VDC at a low duty ratio, resulting in minimal switching loss. The closed-loop steady-state and transient parameters at the output were analyzed and compared using modern evolutionary algorithms. The power range upheld throughout the circuit is around (300–350) W. The battery is assumed to have an impedance model of Resistor-Capacitor (RC) load with a serial range of 12 V and 7 Ah. The proposed converter achieves higher conversion efficiency by the Maximum Power Pont Tracking (MPPT) and NSGA-II/MNSGA-II (non-dominated sorting genetic algorithm) based controller algorithm for tuning the optimal design value and is validated in a MATLAB Simulink platform. In this work, we analyze closed-loop systems under the mentioned power range. The MPPT with an algorithm-based controller tends to trigger the switch in the closed-loop system to get the optimized output. The Maximum Power Point (MPP) technique implemented is an incremental conductance method for extracting solar PV power and improving load performance. Consequently, the proposed evolutionary optimization algorithm steady-state ripple factor response of the proposed MNSGA-II has a lower output side, thus achieving around 98% of the controller implementation efficiency. 

Evolutionary Algorithm Based Z-Source DC-DC Boost Converter for Charging EV Battery

Feed Forward Back Propagation Neural Network (FFBPNN) Optimized with Woodpecker Mating Algorithm is proposed in this manuscript for Dynamic Cluster-Based Secure Routing in Wireless Sensor Networks (FFBPNN-WMA-ECHC-WSN). The proposed method contains three different stages: first stage is dynamically clustered sensor network formation based on FFBPNN, second stage is Key generation for data encryption and decryption using Elliptic Curve-based Hill Cipher (ECHC), third stage is Homomorphic encryption scheme, which is used to deliver the aggregated data in-time. Woodpecker Mating Algorithm (WMA) is proposed to optimize the parameter values of FFBPNN. The proposed FFBPNN-WMA ECHC-WSN method was implemented in NS2 tool and its efficiency is evaluated under some metrics, like alive nodes, packet drop, network lifetime, delay, throughput, and energy consumption, Packet Delivery Ratio (PDR). The performance of FFBPNN-WMA-ECHC-WSN method provides lower delay 99.01%, 98.34%, 95.23% and 97.45%, and higher throughput 97.25%, 90.12%, 89.39% and 95.47% compared with existing models, such as EHCERA-SDT-WSN, DSA-ECC-PSO-SDT-WSN, IPECC-PDF-ABC-SDT-WSN and IBFA-LDCSN-BSHHO-SDT-WSN, respectively. 

FFBP Neural Network Optimized with Woodpecker Mating Algorithm for Dynamic Cluster-based Secure Routing in WSN

At present, real time navigation systems rely on kalman filtering for integrating global positioning system (GPS) and inertial navigation system (INS) in order to provide feasible solution for reliable navigation. But there exist some inadequacies related to the stochastic error models of inertial sensors, requirement of prior knowledge in fusing data and long design time. Moreover, recently artificial intelligence (AI) techniques based on static neural networks were suggested in place of kalman filter. But there also exist drawbacks like inadequate training during GPS absence, less reliability and more training time. To solve this problem, a new dynamic architecture based on Input-delayed model has been developed. This paper, therefore proposes a scheme based on Improved Input-delayed dynamic neural network (IIDDNN) to model the samples of INS values with respect to GPS values as target vectors (Normal mode). This model is capable of providing a reliable and accurate solution especially for GPS absence (prediction mode). The prediction mode output obtained using IIDDNN is compared with the performance of normal mode of IIDDNN. The proposed IIDDNN model is evaluated using test data of fixed trajectory path for an aircraft vehicle.

Improved Input-Delayed Dynamic Neural Network for GPS/INS Integration

/pdf/performance-analysis-of-various-artificial-intelligent-4hegajhg90.pdf

Performance Analysis of Various Artificial Intelligent Neural Networks for GPS/INS Integration

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