An Intelligent Spatial Proximity System Using Neurofuzzy Classifiers and Contextual Information

GEOMATICA ◽  
2015 ◽  
Vol 69 (3) ◽  
pp. 285-296 ◽  
Author(s):  
F. Barouni ◽  
B. Moulin
Keyword(s):  
Fuzzy Systems ◽  
Expert Knowledge ◽  
Contextual Information ◽  
Complete Solution ◽  
Spatial Relations ◽  
Training Data ◽  
Spatial Proximity ◽  
Membership Functions ◽  
Novel Approach ◽  
Neurofuzzy Systems

In this paper, we propose a novel approach to reasoning with the concepts of spatial proximity. The approach is based on contextual information and uses a neurofuzzy classifier to handle the uncertainty aspect of proximity. Neurofuzzy systems are a combination of neural networks and fuzzy systems and effectively incor porate the advantages of both techniques. Although fuzzy systems are focused on knowledge rep re sen ta tion, they do not allow for the estimation of membership functions. Conversely, neuronal networks use powerful learning techniques but are not able to explain how results are obtained. Neurofuzzy systems ben e fit from both techniques by using neuronal network training data to generate membership functions and by using fuzzy rules to represent expert knowledge. Moreover, contextual information is collected from a knowl edge base. The neurofuzzy classifier is used to compute the membership function parameters of the spatial relations fuzzy quantifiers. The complete solution that we propose is integrated in a geographic information sys tem (GIS), enhanced with proximity-reasoning. Our approach is used in the telecommunication domain and particularly in fiber optic monitoring systems. In such systems, a user needs to qualify the distance between events reported by sensors and the surrounding objects of the environment, in order to form spatiotemporal pat terns. These patterns are defined to help users making decisions pertaining to operations, such as optimizing the assignment of emergency crews.

scholarly journals An Intelligent Spatial Proximity System Using Neurofuzzy Classifiers and Contextual Information

2014 ◽  
Vol XL-2 ◽  
pp. 107-114 ◽  
Author(s):  
F. Barouni ◽  
B. Moulin
Keyword(s):  
Fuzzy Systems ◽  
Expert Knowledge ◽  
Contextual Information ◽  
Complete Solution ◽  
Fuzzy Rules ◽  
Training Data ◽  
Spatial Proximity ◽  
Membership Functions ◽  
Data Set ◽  
Neurofuzzy Systems

In this paper, we propose a novel approach to reason with spatial proximity. The approach is based on contextual information and uses a neurofuzzy classifier to handle the uncertainty aspect of proximity. Neurofuzzy systems are a combination of neural networks and fuzzy systems and incorporate the advantages of both techniques. Although fuzzy systems are focused on knowledge representation, they do not allow the estimation of membership functions. Conversely, neuronal networks use powerful learning techniques but they are not able to explain how results are obtained. Neurofuzzy systems benefit from both techniques by using training data to generate membership functions and by using fuzzy rules to represent expert knowledge. Moreover, contextual information is collected from a knowledge base. The complete solution that we propose is integrated in a GIS, enhancing it with proximity reasoning. From an application perspective, the proposed approach was used in the telecommunication domain and particularly in fiber optic monitoring systems. In such systems, a user needs to qualify the distance between a fiber break and the surrounding objects of the environment to optimize the assignment of emergency crews. The neurofuzzy classifier has been used to compute the membership function parameters of the contextual information inputs using a training data set and fuzzy rules.

scholarly journals Combining Deep Learning and Qualitative Spatial Reasoning to Learn Complex Structures from Sparse Examples with Noise

2019 ◽  
Vol 33 ◽  
pp. 2911-2918 ◽  
Author(s):  
Nikhil Krishnaswamy ◽  
Scott Friedman ◽  
James Pustejovsky
Keyword(s):  
Deep Learning ◽  
Heuristic Search ◽  
Spatial Reasoning ◽  
Spatial Relations ◽  
Search Space ◽  
Training Data ◽  
Learning Approaches ◽  
Qualitative Spatial Reasoning ◽  
Heuristic Search Algorithms ◽  
Novel Approach

Many modern machine learning approaches require vast amounts of training data to learn new concepts; conversely, human learning often requires few examples—sometimes only one—from which the learner can abstract structural concepts. We present a novel approach to introducing new spatial structures to an AI agent, combining deep learning over qualitative spatial relations with various heuristic search algorithms. The agent extracts spatial relations from a sparse set of noisy examples of block-based structures, and trains convolutional and sequential models of those relation sets. To create novel examples of similar structures, the agent begins placing blocks on a virtual table, uses a CNN to predict the most similar complete example structure after each placement, an LSTM to predict the most likely set of remaining moves needed to complete it, and recommends one using heuristic search. We verify that the agent learned the concept by observing its virtual block-building activities, wherein it ranks each potential subsequent action toward building its learned concept. We empirically assess this approach with human participants’ ratings of the block structures. Initial results and qualitative evaluations of structures generated by the trained agent show where it has generalized concepts from the training data, which heuristics perform best within the search space, and how we might improve learning and execution.

A PERSPECTIVE AND CRITIQUE OF ADAPTIVE NEUROFUZZY SYSTEMS USED FOR MODELLING AND CONTROL APPLICATIONS

1995 ◽  
Vol 06 (02) ◽  
pp. 197-220 ◽  
Author(s):  
M. BROWN ◽  
C.J. HARRIS
Keyword(s):  
Fuzzy Systems ◽  
Inductive Learning ◽  
Training Data ◽  
Learning Theories ◽  
Learning Rules ◽  
Research Questions ◽  
And Control ◽  
Neurofuzzy Networks ◽  
Neurofuzzy Systems ◽  
Made In

This paper outlines some of the theoretical and practical developments being made in neurofuzzy systems. As the name suggests, neurofuzzy networks were developed by fusing the ideas that originated in the fields of neural and fuzzy systems. A neurofuzzy network attempts to combine the transparent, linguistic, symbolic representation associated with fuzzy logic with the architecture and learning rules commonly used in neural networks. These hybrid structures have both a qualitative and a quantitative interpretation and can overcome some of the difficulties associated with solely neural algorithms which can usually be regarded as black box mappings, and with fuzzy systems where few modelling and learning theories existed. Both B-spline and Gaussian Radial Basis Function networks can be regarded as neurofuzzy systems and soft inductive learning algorithms can be used to extract unknown, qualitative information about the relationships contained in the training data. In a similar manner, qualitative rules or information about the network’s structure can be used to initialise the system. These areas, coupled with the extensive work being carried out on theoretically analysing their modelling, convergence and stability properties means that this research topic is highly applicable in “intelligent” modelling and control problems. Apart from outlining this work, the paper also discusses a wide variety of open research questions and suggests areas where new efforts may be fruitfully applied.

Variances in knowledge-based interval type 2 Gaussian fuzzy on linear regression models

2021 ◽  
pp. 1-14
Author(s):  
Kevin Otieno Gogo ◽  
Lawrence Nderu ◽  
Makau Mutua
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Linear Regression ◽  
Predictive Models ◽  
Fuzzy Systems ◽  
Expert Knowledge ◽  
Knowledge Bases ◽  
Training Data ◽  
Interval Type

Fuzzy logic is a branch of artificial intelligence that has been used extensively in developing Fuzzy systems and models. These systems usually offer artificial intelligence based on the predictive mathematical models used; in this case linear regression mathematical model. Interval type 2 Gaussian fuzzy logic is a fuzzy logic that utilizes Gaussian upper membership function and the lower membership function, with a footprint of uncertainty in between the Gaussian membership functions. The artificial intelligence solutions predicted by these interval type 2 fuzzy systems depends on the training and the resultant linear regression mathematical model developed, which usually extract their training data from the expert knowledge stored in their knowledge bases. The variances in the expert knowledge stored in these knowledge-bases usually affect the overall accuracy of the linear regression predictive models of these systems, due to the variances in the training data. This research therefore establishes the extent that these variances in knowledge bases affect the predictive accuracy of these models, with a case study on knowledge bases used to predict learners’ knowledge level abilities. The calculated linear regression predictive models show that for every variance in the knowledge base, there occurs a change in linear regression predictive model with an intercept value factor commensurate to the variances and their respective weights in the knowledge bases.

scholarly journals Autonomous flight cycles and extreme landings of airliners beyond the current limits and capabilities using artificial neural networks

2021 ◽  
Author(s):  
Haitham Baomar ◽  
Peter J. Bentley
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Training Data ◽  
Aviation Industry ◽  
Control Models ◽  
Novel Approach ◽  
Network Modules ◽  
Artificial Neural

AbstractWe describe the Intelligent Autopilot System (IAS), a fully autonomous autopilot capable of piloting large jets such as airliners by learning from experienced human pilots using Artificial Neural Networks. The IAS is capable of autonomously executing the required piloting tasks and handling the different flight phases to fly an aircraft from one airport to another including takeoff, climb, cruise, navigate, descent, approach, and land in simulation. In addition, the IAS is capable of autonomously landing large jets in the presence of extreme weather conditions including severe crosswind, gust, wind shear, and turbulence. The IAS is a potential solution to the limitations and robustness problems of modern autopilots such as the inability to execute complete flights, the inability to handle extreme weather conditions especially during approach and landing where the aircraft’s speed is relatively low, and the uncertainty factor is high, and the pilots shortage problem compared to the increasing aircraft demand. In this paper, we present the work done by collaborating with the aviation industry to provide training data for the IAS to learn from. The training data is used by Artificial Neural Networks to generate control models automatically. The control models imitate the skills of the human pilot when executing all the piloting tasks required to pilot an aircraft between two airports. In addition, we introduce new ANNs trained to control the aircraft’s elevators, elevators’ trim, throttle, flaps, and new ailerons and rudder ANNs to counter the effects of extreme weather conditions and land safely. Experiments show that small datasets containing single demonstrations are sufficient to train the IAS and achieve excellent performance by using clearly separable and traceable neural network modules which eliminate the black-box problem of large Artificial Intelligence methods such as Deep Learning. In addition, experiments show that the IAS can handle landing in extreme weather conditions beyond the capabilities of modern autopilots and even experienced human pilots. The proposed IAS is a novel approach towards achieving full control autonomy of large jets using ANN models that match the skills and abilities of experienced human pilots and beyond.

scholarly journals HPM: A Hybrid Model for User’s Behavior Prediction Based on N-Gram Parsing and Access Logs

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Sonia Setia ◽  
Verma Jyoti ◽  
Neelam Duhan
Keyword(s):  
Web Mining ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Web Pages ◽  
Continuous Growth ◽  
Novel Approach ◽  
Content Mining ◽  
Long Access ◽  
N Gram ◽  
The Individual

The continuous growth of the World Wide Web has led to the problem of long access delays. To reduce this delay, prefetching techniques have been used to predict the users’ browsing behavior to fetch the web pages before the user explicitly demands that web page. To make near accurate predictions for users’ search behavior is a complex task faced by researchers for many years. For this, various web mining techniques have been used. However, it is observed that either of the methods has its own set of drawbacks. In this paper, a novel approach has been proposed to make a hybrid prediction model that integrates usage mining and content mining techniques to tackle the individual challenges of both these approaches. The proposed method uses N-gram parsing along with the click count of the queries to capture more contextual information as an effort to improve the prediction of web pages. Evaluation of the proposed hybrid approach has been done by using AOL search logs, which shows a 26% increase in precision of prediction and a 10% increase in hit ratio on average as compared to other mining techniques.

On switched control of discrete-time Takagi-Sugeno fuzzy systems with unknown membership functions

2018 ◽  
Author(s):  
Diogo R. de Oliveira ◽  
Gilberto R. dos Santos ◽  
Marcelo C. M. Teixeira ◽  
Edvaldo Assuncao ◽  
Rodrigo Cardim ◽  
...  
Keyword(s):  
Discrete Time ◽  
Fuzzy Systems ◽  
Membership Functions ◽  
Switched Control ◽  
Takagi Sugeno

scholarly journals Removal of Cochannel Interference Using Probabilistic Latent Component Analysis in Passive Bistatic Radar

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Tao Ying ◽  
Xuebao Wang ◽  
Wei Tian ◽  
Cheng Zhou
Keyword(s):  
Target Detection ◽  
Reference Signal ◽  
Component Analysis ◽  
Cochannel Interference ◽  
Training Data ◽  
Bistatic Radar ◽  
Frequency Channel ◽  
Novel Approach ◽  
Passive Bistatic Radar

This paper examines the problem of cancellation of cochannel interference (CCI) present in the same frequency channel as the signal of interest, which may bring a reduction in the performance of target detection, in passive bistatic radar. We propose a novel approach based on probabilistic latent component analysis for CCI removal. The highlight is that removing CCI is considered as reconstruction, and extraction of Doppler-shifted and time-delayed replicas of the reference signal exploited fully as training data. The results of the simulation show that the developed method is effective.

Sign in / Sign up

Export Citation Format

Share Document