Miniature robotic blimps, as one type of lighter-than-air aerial vehicles, have attracted increasing attention in the science and engineering community for their enhanced safety, extended endurance, and quieter operation compared to quadrotors. Accurately modeling the dynamics of these robotic blimps poses a significant challenge due to the complex aerodynamics stemming from their large lifting bodies. Traditional first-principle models have difficulty obtaining accurate aerodynamic parameters and often overlook high-order nonlinearities, thus coming to its limit in modeling the motion dynamics of miniature robotic blimps. To tackle this challenge, this letter proposes the Auto-tuning Blimp-oriented Neural Ordinary Differential Equation method (ABNODE), a data-driven approach that integrates first-principle and neural network modeling. Spiraling motion experiments of robotic blimps are conducted, comparing the ABNODE with first-principle and other data-driven benchmark models, the results of which demonstrate the effectiveness of the proposed method.

PyliteML is a suite of Machine Learning tools that will help you make your IoT Applications even smarter. Edge of Network IoT solutions must become more autonomous and help end users make even smarter and more decisions. But we don't need to tell you all the benefits of Machine Learning…you probably know all about it already. We have some cool tutorials on our Youtube channel and below is an overview of the way we've implemented it. In this video, we follow the 3 steps necessary to create and set up a model in Pybytes.

--With the rapid evolution of wireless mobile devices, it emerges stronger incentive to design proper collaboration mechanisms among the intelligent agents. Following their individual observations, multiple intelligent agents could cooperate and gradually approach the final collective objective through continuously learning from the environment. In that regard, independent reinforcement learning (IRL) is often deployed within the multi-agent collaboration to alleviate the dilemma of non-stationary learning environment. However, behavioral strategies of the intelligent agents in IRL could only be formulated upon their local individual observations of the global environment, and appropriate communication mechanisms must be introduced to reduce their behavioral localities. In this paper, we tackle the communication problem among the intelligent agents in IRL by jointly adopting two mechanisms with different scales. For the large scale, we introduce the stigmergy mechanism as an indirect communication bridge among the independent learning agents and carefully design a mathematical representation to indicate the impact of digital pheromone. For the small scale, we propose a conflict-avoidance mechanism between adjacent agents by implementing an additionally embedded neural network to provide more opportunities for participants with higher action priorities. Besides, we also present a federal training method to effectively optimize the neural networks within each agent in a decentralized manner . Finally, we establish a simulation scenario where a number of mobile agents in a certain area move automatically to form a specified target shape, and demonstrate the superiorities of our proposed methods through extensive simulations. I NTRODUCTION With the rapid development of mobile wireless communication and IoTs (Internet of Things) technologies, many scenarios gradually arise where the collaboration among the involved intelligent agents is highly required, such as the deployment of unmanned aerial vehicles (UA Vs) [1]-[3], the distributed control in the field of industry automation [4]-[6], and mobile crowd sensing and computing (MCSC) [7], [8]. In these scenarios, traditional centralized control methods are usually impracticable because of the restriction from limited computing resources as well as the demand for ultra-low latency and ultra-high reliability. As an alternative, multi-agent collaboration can be introduced into these scenarios to reduce the pressure at the central controller side. As one of the primary goals in the field of artificial intelligence (AI), assisting autonomous agents to act optimally through the "trial-and-error" interaction process with the expected environment is regarded as an important target of reinforcement learning (RL) [9]-[11].

We propose a neural network architecture, called TransNet, that combines planning and model learning for solving Partially Observable Markov Decision Processes (POMDPs) with non-uniform system dynamics. The past decade has seen a substantial advancement in solving POMDP problems. However, constructing a suitable POMDP model remains difficult. Recently, neural network architectures have been proposed to alleviate the difficulty in acquiring such models. Although the results are promising, existing architectures restrict the type of system dynamics that can be learned --that is, system dynamics must be the same in all parts of the state space. TransNet relaxes such a restriction. Key to this relaxation is a novel neural network module that classifies the state space into classes and then learns the system dynamics of the different classes. TransNet uses this module together with the overall architecture of QMDP-Net[1] to allow solving POMDPs that have more expressive dynamic models, while maintaining efficient data requirement. Its evaluation on typical benchmarks in robot navigation with initially unknown system and environment models indicates that TransNet substantially out-performs the quality of the generated policies and learning efficiency of the state-of-the-art method QMDP-Net.

This tutorial is based on the Neural Network Module, available on ATOMS. This Neural Network Module is based on the book "Neural Network Design" book by Martin T. Hagan. A function is implemented in neural network module to simplify the plotting of 2 groups of data points. First, we split the data to the source (P), and target (T). We transpose the data to match the format required by the module.

Please could you argument more? Note that the main aim of the extension is to provide an interface, if your problem is with the fact that the NN implementation is a very simple one. Also note that this is not targeting computer vision problems, but mostly classifications and regressions with a limited number of input variables. Nothing prevents swapping the underlying neural network implementation with a more advanced one in the future. EDIT: However note how for the simple problems this aims to solve, which are very important business problems for websites and apps developers btw, using more powerful NNs may not make an actual difference.

Suppose you were to watch a stream of numbers… and given the previous number you saw you had to predict what the next number should be. For example, suppose you saw 1,2,1,2,… We might guess: 1. Or perhaps, what if you saw 0,0,1,2,3…? Should it be 4? It almost feels like a domino effect. In this post we walk through predicting a specific type of spike in domain queries associated with some botnets.