scholarly journals White-tailed deer sex identification from faecal DNA and pellet morphometry by neural network and fuzzy logic analyses

2019 ◽  
Vol 29 ◽  
pp. 1-12
Author(s):  
Dania Vega ◽  
Sonia Gallina ◽  
Miguel Correa ◽  
M. Parra ◽  
Isaias Chairez
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Faecal Pellet ◽  
Gene Marker ◽  
Faecal Dna ◽  
Sry Gene ◽  
Faecal Pellets ◽  
Non Invasive ◽  
The Neural Network ◽  
Fuzzy Logic Analysis

Knowing the sex of white-tailed deer (Odocoileus virginianus) individuals can provide information to set harvesting rates and management activities. Therefore, the aim of this study is to identify the sex through classification function by using faecal pellet morphometry. Faeces were collected for 12 months in Durango, Mexico; their morphometric variables were measured, the faecal DNA was extracted, and the SRY gene marker was amplified to identify sex. A neural network and fuzzy logic sex classification functions were obtained. The outputs were validated with the SRY gene results. Data from adults in the winter were used to obtain the classification functions. Classification functions could accurately classify sex in 94.4% with neural networks and 86.9% with fuzzy logic. The neural network classified more accurately the sex of adult white-tailed deer studied in winter with the faecal pellets morphometry than with the fuzzy logic analysis. This technique can be a tool for non-invasive studies and monitoring of populations.

scholarly journals Design and implementation of an intelligent vision and sorting system

2009 ◽  
Author(s):  
◽  
Zhi Li
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Support Vector Machines ◽  
Robot Control ◽  
Support Vector ◽  
Design And Implementation ◽  
The Neural Network ◽  
Vector Machines ◽  
Control Computer ◽  
Sorting System

This research focuses on the design and implementation of an intelligent machine vision and sorting system that can be used to sort objects in an industrial environment. Machine vision systems used for sorting are either geometry driven or are based on the textural components of an object’s image. The vision system proposed in this research is based on the textural analysis of pixel content and uses an artificial neural network to perform the recognition task. The neural network has been chosen over other methods such as fuzzy logic and support vector machines because of its relative simplicity. A Bluetooth communication link facilitates the communication between the main computer housing the intelligent recognition system and the remote robot control computer located in a plant environment. Digital images of the workpiece are first compressed before the feature vectors are extracted using principal component analysis. The compressed data containing the feature vectors is transmitted via the Bluetooth channel to the remote control computer for recognition by the neural network. The network performs the recognition function and transmits a control signal to the robot control computer which guides the robot arm to place the object in an allocated position. The performance of the proposed intelligent vision and sorting system is tested under different conditions and the most attractive aspect of the design is its simplicity. The ability of the system to remain relatively immune to noise, its capacity to generalize and its fault tolerance when faced with missing data made the neural network an attractive option over fuzzy logic and support vector machines.

Neural Network Based Intelligent Learning of Fuzzy Logic Controller Parameters

2004 ◽  
Author(s):  
Manish Kumar ◽  
Devendra P. Garg
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Network Architecture ◽  
Fuzzy Logic Controller ◽  
Rule Base ◽  
Neural Network Architecture ◽  
The Neural Network ◽  
Neuro Fuzzy ◽  
Learning Capabilities ◽  
Optimal Signal

Design of an efficient fuzzy logic controller involves the optimization of parameters of fuzzy sets and proper choice of rule base. There are several techniques reported in recent literature that use neural network architecture and genetic algorithms to learn and optimize a fuzzy logic controller. This paper presents methodologies to learn and optimize fuzzy logic controller parameters that use learning capabilities of neural network. Concepts of model predictive control (MPC) have been used to obtain optimal signal to train the neural network via backpropagation. The strategies developed have been applied to control an inverted pendulum and results have been compared for two different fuzzy logic controllers developed with the help of neural networks. The first neural network emulates a PD controller, while the second controller is developed based on MPC. The proposed approach can be applied to learn fuzzy logic controller parameter online via the use of dynamic backpropagation. The results show that the Neuro-Fuzzy approaches were able to learn rule base and identify membership function parameters accurately.

scholarly journals The control to aggregates of pumping stations using a regulator based on a neural network with fuzzy logic

2019 ◽  
Vol 102 ◽  
pp. 03007
Author(s):  
Vladlen Kuznetsov ◽  
Sergey Dyadun ◽  
Valentin Esilevsky
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Simulation Modeling ◽  
Neural Controller ◽  
The Neural Network ◽  
Pumping Stations ◽  
Fuzzy Neural ◽  
Pumping Units ◽  
Theory Of Fuzzy Sets

A pumping station control system is considered using a controller based on a fuzzy logic neural network. The simulation of the classical and fuzzy regulators. The possibility of the implementation of the controller in the form of an adaptive multilayer neural network is shown. The use of the theory of fuzzy sets in combination with the theory of neural networks to create a fuzzy-neural regulator to control pumping units provides a promising approach. Simulation modeling and real operation have shown that fuzzy-logic regulators have a number of advantages over classical regulators, which allow the use of form and limitations. Using the neural network model allows you to add the properties of adaptability and learning. The fuzzy-neural controller for controlling pumping units is promising in terms of efficiency and safety by controlling pumping stations.

Neural Networks for Content-Based Image Retrieval

2011 ◽  
pp. 252-272 ◽  
Author(s):  
Brijesh Verma ◽  
Siddhivinayak Kulkarni
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
The Neural Network ◽  
Benchmark Database ◽  
Overall Performance ◽  
Critical Literature

This chapter introduces neural networks for Content-Based Image Retrieval (CBIR) systems. It presents a critical literature review of both the traditional and neural network based techniques that are used in retrieving the images based on their content. It shows how neural networks and fuzzy logic can be used in interpretation of queries, feature extraction and classification of features by describing a detailed research methodology. It investigates a neural network based technique in conjunction with fuzzy logic to improve the overall performance of the CBIR systems. The results of the investigation on a benchmark database with a comparative analysis are presented in this chapter. The methodologies and results presented in this chapter will allow researchers to improve and compare their methods and it will also allow system developers to understand and implement the neural network and fuzzy logic based techniques for content based image retrieval.

scholarly journals PV Module Fault Detection Using Combined Artificial Neural Network and Sugeno Fuzzy Logic

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2150
Author(s):  
Romênia G. Vieira ◽  
Mahmoud Dhimish ◽  
Fábio M. U. de Araújo ◽  
Maria I. S. Guerra
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Fault Detection ◽  
Machine Learning Techniques ◽  
Photovoltaic Systems ◽  
The Neural Network ◽  
Learning Techniques ◽  
Network Output ◽  
Input Variables ◽  
Power Point

This work introduces a new fault detection method for photovoltaic systems. The method identifies short-circuited modules and disconnected strings on photovoltaic systems combining two machine learning techniques. The first algorithm is a multilayer feedforward neural network, which uses irradiance, ambient temperature, and power at the maximum power point as input variables. The neural network output enters a Sugeno type fuzzy logic system that precisely determines how many faulty modules are occurring on the power plant. The proposed method was trained using a simulated dataset and validated using experimental data. The obtained results showed 99.28% accuracy on detecting short-circuited photovoltaic modules and 99.43% on detecting disconnected strings.

Navigational path analysis of mobile robots using an adaptive neuro-fuzzy inference system controller in a dynamic environment

2010 ◽  
Vol 224 (6) ◽  
pp. 1369-1381 ◽  
Author(s):  
D R Parhi ◽  
M K Singh
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Mobile Robots ◽  
Fuzzy Inference ◽  
Dynamic Environment ◽  
Fuzzy Logic System ◽  
Inference System ◽  
The Neural Network ◽  
Neuro Fuzzy ◽  
Logic System

This article focuses on the navigational path analysis of mobile robots using the adaptive neuro-fuzzy inference system (ANFIS) in a cluttered dynamic environment. In the ANFIS controller, after the input layer there is a fuzzy layer and the rest of the layers are neural network layers. The adaptive neuro-fuzzy hybrid system combines the advantages of the fuzzy logic system, which deals with explicit knowledge that can be explained and understood, and those of the neural network, which deals with implicit knowledge that can be acquired by learning. The inputs to the fuzzy logic layer include the front obstacle distance, the left obstacle distance, the right obstacle distance, and target steering. A learning algorithm based on the neural network technique has been developed to tune the parameters of fuzzy membership functions, which smooth the trajectory generated by the fuzzy logic system. Using the developed ANFIS controller, the mobile robots are able to avoid static and dynamic obstacles and reach the target successfully in cluttered environments. The experimental results agree well with the simulation results; this proves the authenticity of the theory developed.

scholarly journals A non-intrusive load identification algorithm based on deep learning and a compound feature

2021 ◽  
Vol 256 ◽  
pp. 02034
Author(s):  
Tong Jiang ◽  
Ruyu Bai
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Identification Algorithm ◽  
Load Identification ◽  
Current Harmonic ◽  
Non Invasive ◽  
The Neural Network ◽  
Single Feature ◽  
Trajectory Features ◽  
High Frequency Sampling

Aiming at the limitations of using a single feature for load identification, a non-intrusive load identification algorithm based on deep learning and compound features is proposed. The pixelated V-I trajectory characteristics and current harmonic characteristics are extracted by analyzing the load data under high-frequency sampling. Using the feature extraction capabilities of neural networks, the combination of pixelated V-I trajectory features and current harmonic features is realized. Finally, the composite feature is used as the new load feature to train the neural network for non-invasive load identification. The experimental results show that the two-layer neural network constructed by the algorithm can take advantage of the complementarity between the two features, thereby improving the load identification ability.

scholarly journals Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Kevin M Elias ◽  
Wojciech Fendler ◽  
Konrad Stawiski ◽  
Stephen J Fiascone ◽  
Allison F Vitonis ◽  
...  
Keyword(s):  
Neural Network ◽  
Ovarian Cancer ◽  
Predictive Value ◽  
Clinical Samples ◽  
Small Rna Sequencing ◽  
Serum Samples ◽  
Non Invasive ◽  
Metastatic Lesions ◽  
Diagnostic Potential ◽  
The Neural Network

Recent studies posit a role for non-coding RNAs in epithelial ovarian cancer (EOC). Combining small RNA sequencing from 179 human serum samples with a neural network analysis produced a miRNA algorithm for diagnosis of EOC (AUC 0.90; 95% CI: 0.81–0.99). The model significantly outperformed CA125 and functioned well regardless of patient age, histology, or stage. Among 454 patients with various diagnoses, the miRNA neural network had 100% specificity for ovarian cancer. After using 325 samples to adapt the neural network to qPCR measurements, the model was validated using 51 independent clinical samples, with a positive predictive value of 91.3% (95% CI: 73.3–97.6%) and negative predictive value of 78.6% (95% CI: 64.2–88.2%). Finally, biologic relevance was tested using in situ hybridization on 30 pre-metastatic lesions, showing intratumoral concentration of relevant miRNAs. These data suggest circulating miRNAs have potential to develop a non-invasive diagnostic test for ovarian cancer.

Research on the Automotive Suspension Based on BP Neural Network

2013 ◽  
Vol 341-342 ◽  
pp. 478-481
Author(s):  
Tai Hao Li ◽  
He Pan
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Bp Neural Network ◽  
Fuzzy Neural Network ◽  
Air Suspension ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
Intelligence Theory ◽  
Neural Network Algorithm ◽  
Automotive Suspension

This article uses the application of artificial intelligence theory to research on the air suspension system, constructing the structure of control system, and the study of the neural network algorithm is simulation for its study of results. The fusion of fuzzy logic and neural network consist of the fuzzy neural network, which has the advantages of fuzzy logic and neural network.

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