This paper presents a distinctive hardware-efficient, high-accuracy, and power-optimized implementation of the hyperbolic tangent activation function based on Chebyshev polynomial approximation, optimized for deployment in RNN controllers for DC-AC inverters. The proposed method overcomes the limitations of conventional approaches by leveraging fixed-point arithmetic and Clenshaw's recurrence for numerically stable evaluation. Two Verilog-based architectures are developed and synthesized on an Intel FPGA: a compact FSM-based design that evaluates a single high-degree polynomial, and a low-latency interval-based implementation that utilizes multiple lower-degree polynomials. Both designs are validated against MATLAB references and demonstrate high accuracy. Resource utilization and power analysis reveal that the FSM-based implementation consumes significantly fewer resources compared to existing methods. Experimental results confirm that the proposed Chebyshev designs achieve bit-level accuracy, a reduced hardware, and low power consumption, making them ideal for artificial intelligence-enhanced power electronics applications requiring real-time performance in resource-constrained environments.

This paper introduces a novel parallel trajectory mechanism that combines Levenberg-Marquardt and Forward Accumulation Through Time algorithms to train a recurrent neural network controller in a closed-loop control system by distributing the calculation of trajectories across Central Processing Unit (CPU) cores/workers depending on the computing platforms, computing program languages, and software packages available. Without loss of generality, the recurrent neural network controller of a grid-connected converter for solar integration to a power system was selected as the benchmark test closed-loop control system. Two software packages were developed in Matlab and C++ to verify and demonstrate the efficiency of the proposed parallel training method. The training of the deep neural network controller was migrated from a single workstation to both cloud computing platforms and High-Performance Computing clusters. The training results show excellent speed-up performance, which significantly reduces the training time for a large number of trajectories with high sampling frequency, and further demonstrates the effectiveness and scalability of the proposed parallel mechanism.

This paper introduces a novel weight-dropout approach to train a neural network controller in real-time closed-loop control and to accelerate the embedded implementation for a solar inverter. The essence of the approach is to drop small-magnitude weights of neural network controllers during training with the goal of minimizing the required numbers of connections and guaranteeing the convergence of the neural network controllers. In order not to affect the convergence of neural network controllers, only non-diagonal elements of the neural network weight matrices were dropped. The dropout approach was incorporated into Levenberg-Marquardt and Forward Accumulation Through Time algorithms to train the neural network controller for trajectory tracking more efficiently. The Field Programmable Gate Array (FPGA) implementation on the Intel Cyclone V board shows significant improvement in terms of computation and resource requirements using the sparse weight matrices after dropout, which makes the neural network controller more suitable in an embedded environment.

This article investigates the local stability and local convergence of a class of neural network (NN) controllers with error integrals as inputs for reference tracking. It is formally proved that if the input of the NN controller consists exclusively of error terms, the control system shows a non-zero steady-state error for any constant reference except for one specific point, for both single-layer and multi-layer NN controllers. It is further proved that adding error integrals to the input of the (single- and multi-layers) NN controller is one sufficient way to remove the steady-state error for any constant reference. Due to the nonlinearity of the NN controllers, the NN control systems are linearized at the equilibrium points. We provide proof that if all the eigenvalues of the linearized NN control system have negative real parts, local asymptotic stability and local exponential convergence are guaranteed. Two case studies were explored to verify the theoretical results: a single-layer NN controller in a 1-D system and a four-layer NN controller in a 2-D system applied to renewable energy integration. Simulations demonstrate that when NN controllers and the corresponding generalized proportional-integral (PI) controllers have the same eigenvalues, all control systems exhibit almost the same responses in a small neighborhood of their respective equilibrium points.

We are on the verge of a post-genomics era in which whole protein sequencing will be quickly carried out. Protein se-quencing plays an important role in identifying protein functions, analyzing protein-protein interactions, and characterizing post-translational modifications, etc. The protein sequencing problem is to determine the complete sequence of amino acids in proteins. De novo protein sequencing using top-down and bottom-up tandem mass spectrometry suffers from the problem of producing only partial sequences of target proteins, namely scaffold. In this paper, we explore the possibility of using deep learning techniques to perform the task of predicting amino acids in partially sequenced proteins by two phases. First, our methods involve querying the NCBI Protein Blast server to find closest matching homologous sequences to a scaffold as a training dataset. Second, we train several deep learning models based on a convolutional neural network and long short term memory to predict missing amino acids in the scaffold in the forward and reverse directions. We comprehensively evaluate our proposed methods on an alemtuzumab dataset and our results show that the proposed methods achieve high sequence coverage and high sequence accuracy with 100 % on the the light chain of alemtuzumab scaffold data.

This paper proposes a novel artificial neural network (ANN) based control method for a dc/dc buck converter. The ANN is trained to implement optimal control based on approximate dynamic programming (ADP). Special characteristics of the proposed ANN control include: 1) The inputs to the ANN contain error signals and integrals of the error signals, enabling the ANN to have PI control ability; 2) The ANN receives voltage feedback signals from the dc/dc converter, making the combined system equivalent to a recurrent neural network; 3) The ANN is trained to minimize a cost function over a long time horizon, making the ANN have a stronger predictive control ability than a conventional predictive controller; 4) The ANN is trained offline, preventing the instability of the network caused by weight adjustments of an on-line training algorithm. The ANN performance is evaluated through simulation and hardware experiments and compared with conventional control methods, which shows that the ANN controller has a strong ability to track rapidly changing reference commands, maintain stable output voltage for a variable load, and manage maximum duty-ratio and current constraints properly.

This paper focuses on current control in a permanent-magnet synchronous motor (PMSM). This paper has two main objectives: the first objective is to develop a neural-network (NN) vector controller to overcome the decoupling inaccuracy problem associated with the conventional proportional-integral-based vector-control methods. The NN is developed using the full dynamic equation of a PMSM, and trained to implement optimal control based on approximate dynamic programming. The second objective is to evaluate the robust and adaptive performance of the NN controller against that of the conventional standard vector controller under motor parameter variation and dynamic control conditions by: 1) simulating the behavior of a PMSM typically used in realistic electric vehicle applications and 2) building an experimental system for hardware validation as well as combined hardware and simulation evaluation. The results demonstrate that the NN controller outperforms conventional vector controllers in both simulation and hardware implementation.

This paper investigates how to train a recurrent neural network (RNN) using the Levenberg-Marquardt (LM) algorithm as well as how to implement optimal control of a grid-connected converter (GCC) using an RNN. To successfully and efficiently train an RNN using the LM algorithm, a new forward accumulation through time (FATT) algorithm is proposed to calculate the Jacobian matrix required by the LM algorithm. This paper explores how to incorporate FATT into the LM algorithm. The results show that the combination of the LM and FATT algorithms trains RNNs better than the conventional backpropagation through time algorithm. This paper presents an analytical study on the optimal control of GCCs, including theoretically ideal optimal and suboptimal controllers. To overcome the inapplicability of the optimal GCC controller under practical conditions, a new RNN controller with an improved input structure is proposed to approximate the ideal optimal controller. The performance of an ideal optimal controller and a well-trained RNN controller was compared in close to real-life power converter switching environments, demonstrating that the proposed RNN controller can achieve close to ideal optimal control performance even under low sampling rate conditions. The excellent performance of the proposed RNN controller under challenging and distorted system conditions further indicates the feasibility of using an RNN to approximate optimal control in practical applications.

With the improvement of battery technology over the past two decades and automotive technology advances, more and more vehicle manufacturers have joined in the race to produce new generation of affordable, high-performance Electric Drive Vehicles (EDVs). Permanent Magnet Synchronous Motors (PMSMs) are at the top of AC motors in high performance drive systems for EDVs. Traditionally, a PMSM is controlled with standard decoupled d-q vector control mechanisms. However, recent studies indicate that such mechanisms show serious limitations. This paper investigates how to mitigate such problems using a nested-loop neural network architecture to control a PMSM. The neural network implements a dynamic programming algorithm and is trained using backpropagation through time. The performance of the neural controller is studied for typical vector control conditions and compared with conventional vector control methods, which demonstrates the neural vector control strategy proposed in this paper is effective. Even in a highly dynamic switching environment, the neural vector controller shows strong ability to track rapidly changing reference commands, tolerate system disturbances, and satisfy control requirements for complex EDV drive needs.