Shadow-Cuts Minimization/Maximization and Complex Hopfield Neural Networks

In this paper, we present a general Gaussian N-relay network by allowing relays to communicate to each other and allowing a direct channel between source and destination as compared to the standard diamond network in Nazaroğlu et al. (IEEE Trans Inf Theory 60:6329–6341, 2014) at the cost of extra channel uses. Our main focus is to examine the min-cut bound capacities of the relay network. Very recently, the results in Uykan (IEEE Trans Neural Netw Learn Syst 31:3294–3304, 2020) imply that the GADIA in Babadi and Tarokh (IEEE Trans Inf Theory 56:6228–6252, 2010), a pioneering algorithm in the interference avoidance literature, actually performs max-cut of a given power-domain (nonnegative) link gain matrix in the 2-channel case. Using the results of the diamond network in Nazaroğlu et al. (2014) and the results in Uykan (2020), in this paper, we (i) turn the mutual information maximization problem in the Gaussian N-relay network into an upper bound minimization problem, (ii) propose a modified GADIA-based algorithm to find the min-cut capacity bound and (iii) present an upper and a lower bound to its min-cut capacity bound using the modified GADIA as applied to the defined “squared channel gain matrix/graph”. Some advantages of the proposed modified GADIA-based simple algorithm are as follows: (1) The Gaussian N-relay network can determine the relay clusters in a distributed fashion and (2) the presented upper bound gives an insight into whether allowing the relays to communicate to each other pays off the extra channel uses or not as far as the min-cut capacity bound is concerned. The simulation results confirm the findings. Furthermore, the min-cut upper bound found by the proposed modified-GADIA is verified by the cut-set bounds found by the spectral clustering based solutions as well.

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“…For further details, see [1]. An extension to the complex graph case has very recently been presented in [33].…”

Section: Basic Gadia [2] With 2-channel Casementioning

confidence: 99%

A modified GADIA-based upper-bound to the capacity of Gaussian general N-relay networks

Uykan

Jäntti

2021

Wireless Netw

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“…On two FL models, the usefulness of the suggested strategy to communication optimization is confirmed. When compared to conventional federation learning, the suggested strategy reduced the number of network updates by 60% and sped up the model's convergence by 10.3%, according to extensive testing using the MNIST dataset [13].…”

Section: Introductionmentioning

confidence: 99%

cFL：Data distribution edge clustering algorithm based on deep Federated learning

Xide Wang

2024

jes

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Federated learning (FL) allows end-devices to train local models on their respective local datasets and collaborate with the server to train a global predictive model, thus achieving the goal of machine learning while protecting the privacy and sensitive data of end-devices. However, simultaneous access to the server by a large number of end devices may result in increased transmission latency and some local models may have malicious behavior, converging in the opposite direction to the global model. As a result, additional communication costs can occur during the federation learning process. Existing research has mainly focused on reducing the number of communication rounds or cleaning dirty local data. In order to decrease the overall amount of local updates, we provide an edge-based model cleaning and device clustering strategy in this study. By computing the cosine similarity between local update parameters and global model parameters over a multi-dimensional space, the approach assesses if local updates are necessary while preventing pointless communications. In addition, end devices are grouped together based on where they are connected to the network and connect to the cloud via mobile edge nodes in clusters, therefore lowering the latency associated with high concurrent server access. Convolutional neural networks and Soft max regression are used, for instance, to perform MNIST handwritten digit recognition, and the effectiveness of the suggested approach to enhancing communication efficiency is confirmed. According to experimental findings, the edge-based model cleaning and device clustering technique decreases the quantity of local updates by 60% and speeds up the model's convergence by 10.3% when compared to the conventional FL approach.

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“…Recent advancements in artificial intelligence (AI) and evolutionary computational techniques, collectively referred to as intelligent computational techniques, have introduced innovative solutions for controlling RIP systems. AI has gained popularity in clustering random complex matrices [19] and graphs [20]. There has been a recent surge in the development of an intelligent optimal control approach that integrates the adaptive features of intelligent computation into traditional linear control frameworks.…”

Section: Introductionmentioning

confidence: 99%

Reduced-Order Observer-Based LQR Controller Design for Rotary Inverted Pendulum

Gao,

Xu,

Huang

et al. 2024

CMES

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The Rotary Inverted Pendulum (RIP) is a widely used underactuated mechanical system in various applications such as bipedal robots and skyscraper stabilization where attitude control presents a significant challenge. Despite the implementation of various control strategies to maintain equilibrium, optimally tuning control gains to effectively mitigate uncertain nonlinearities in system dynamics remains elusive. Existing methods frequently rely on extensive experimental data or the designer's expertise, presenting a notable drawback. This paper proposes a novel tracking control approach for RIP, utilizing a Linear Quadratic Regulator (LQR) in combination with a reduced-order observer. Initially, the RIP system is mathematically modeled using the Newton-Euler-Lagrange method. Subsequently, a composite controller is devised that integrates an LQR for generating nominal control signals and a reduced-order observer for reconstructing unmeasured states. This approach enhances the controller's robustness by eliminating differential terms from the observer, thereby attenuating unknown disturbances. Thorough numerical simulations and experimental evaluations demonstrate the system's capability to maintain balance below 50 Hz and achieve precise tracking below 1.4 rad, validating the effectiveness of the proposed control scheme.

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Shadow-Cuts Minimization/Maximization and Complex Hopfield Neural Networks

Cited by 8 publications

References 37 publications

A modified GADIA-based upper-bound to the capacity of Gaussian general N-relay networks

A modified GADIA-based upper-bound to the capacity of Gaussian general N-relay networks

cFL：Data distribution edge clustering algorithm based on deep Federated learning

Reduced-Order Observer-Based LQR Controller Design for Rotary Inverted Pendulum

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