Reinforcement learning based adaptive control method for traffic lights in intelligent transportation

Addressing the requirements and challenges of traffic light control, a reinforcement learning based adaptive optimal control model for traffic lights in intelligent transportation systems is proposed. In the model design, we combined Markov decision process, Q-learning algorithm, and Deep Q-Learning Network (DQN) control theory to establish a comprehensive signal light Adaptive Optimal Control of Signal Lights in Intelligent Transportation Systems (AOCITL) control model. Through simulation experiments on the model and the application of actual road scene data, we have verified the superiority of the model in improving traffic system efficiency and reducing traffic pressure. The experimental results show that compared with traditional fixed cycle signal light control, the adaptive optimal control model based on reinforcement learning can significantly improve the traffic efficiency of roads, reduce the incidence of traffic accidents, and enhance the overall operational effectiveness of urban transportation systems. The proposed method is possible to further optimize the model algorithm, expand its application scope, and promote the development and practical application of intelligent transportation systems

Decoupling Machine Intelligence from Application in IoT devices

Currently, the most prominent model for developing intelligent applications for IoT devices is to have intelligence embedded into the application. This model is characterized by strong coupling between application logic and intelligence implementations in the code of the intelligent application. Alternatively, the intelligence can be taken out of the application and turned into a cloud service that application logic can utilize via standardized Web APIs. This model is characterized by weak coupling between application logic code and intelligence implementation. Strong coupling model makes lifecycle management of intelligence difficult. To update intelligence, usually the whole application must be updated. Cloud based weak coupling model also has multiple faults like the need for constant connectivity to the central cloud or data privacy concerns. In this thesis, local on-device weak coupling model for building intelligent applications and its prototype implementation are presented. The model is based on the concept of intelligent layer. Intelligent layer is a layer between operating system and application layer that provides intelligent services to the processes in application layer. Presented prototype implementation is called intelligence layer service. It is able to serve limited type of machine learning models represented by Open Neural Network Exchange (ONNX) format

Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes

Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.Comment: 28 pages, Published 21 April 2015 at MDPI's journal "Sensors

ANN in Financial Prediction

This paper focuses on the treatment of intelligent systems and their application in the financial area. Types of intelligent systems are numerous, but we will focus on those systems, which based on their ability to learn, are able to predict. The concept of inductive reasoning, how these systems learn and reason inductively, the role and their integration in financial services are some of the concepts that will be addressed. The second and the main part focuses on the application developed in the design of an artificial neural network for financial forecasts. Recognizing the need for better predictive models, not just traditional statistical model, we considered with interest the development of an application that will predict currency exchange rates, USD-ALL, given the time series of real data in years 1995-2012. We test some of the learning algorithms in our system and conclude that one of them is most suitable for this problem. This intelligent system reached to create a relational model of data, on the basis of which is able to output satisfactory results forecast. After the presentation of experimental results, the paper closes with a discussion on possible improvements that could be made in the future

ANN in Financial Prediction

This paper focuses on the treatment of intelligent systems and their application in the financial area. Types of intelligent systems are numerous, but we will focus on those systems, which based on their ability to learn, are able to predict. The concept of inductive reasoning, how these systems learn and reason inductively, the role and their integration in financial services are some of the concepts that will be addressed. The second and the main part focuses on the application developed in the design of an artificial neural network for financial forecasts. Recognizing the need for better predictive models, not just traditional statistical model, we considered with interest the development of an application that will predict currency exchange rates, USD-ALL, given the time series of real data in years 1995-2012. We test some of the learning algorithms in our system and conclude that one of them is most suitable for this problem. This intelligent system reached to create a relational model of data, on the basis of which is able to output satisfactory results forecast. After the presentation of experimental results, the paper closes with a discussion on possible improvements that could be made in the future

Quantitative precipitation analysis and offline gui using neural network system

This project discovers the implementation of Artificial Neural Network (ANN) for forecasting weather based on past relevant data. Neural network is constructed using empirical network architecture and (17) training types. They are such as BFGS quasi-Newton backpropagation, Cyclical order incremental training w/learning functions, Levenberg-Marquardt backpropagation, Resilient backpropagation and others. The ANN has been trained using 2008 weather data and tested with data year 2009. As result, the system has successfully generating accuracy up to 78.69% for quantitative precipitation (QP) prediction. Analysis on time consumption of all those training types is made and shows that Resilient backpropagation with 1.92s training time consumption is the fastest and Cyclical order incremental training w/learning functions with 463.215s is the slowest. This project concluded that ANN is an alternative method in controlling and understanding the way of non-linear set of data and variables to become mutually correlated with each other. It is a powerful yet significant method in embedding intelligent system into application for meteorological tools