Fuzzy Layer Research Articles

Deep fuzzy physics-informed neural networks for forward and inverse PDE problems

As a grid-independent approach for solving partial differential equations (PDEs), Physics-Informed Neural Networks (PINNs) have garnered significant attention due to their unique capability to simultaneously learn from both data and the governing physical equations. Existing PINNs methods always assume that the data is stable and reliable, but data obtained from commercial simulation software often inevitably have ambiguous and inaccurate problems. Obviously, this will have a negative impact on the use of PINNs to solve forward and inverse PDE problems. To overcome the above problems, this paper proposes a Deep Fuzzy Physics-Informed Neural Networks (FPINNs) that explores the uncertainty in data. Specifically, to capture the uncertainty behind the data, FPINNs learns fuzzy representation through the fuzzy membership function layer and fuzzy rule layer. Afterward, we use deep neural networks to learn neural representation. Subsequently, the fuzzy representation is integrated with the neural representation. Finally, the residual of the physical equation and the data error are considered as the two components of the loss function, guiding the network to optimize towards adherence to the physical laws for accurate prediction of the physical field. Extensive experiment results show that FPINNs outperforms these comparative methods in solving forward and inverse PDE problems on four widely used datasets. The demo code will be released at https://github.com/siyuancncd/FPINNs.

Adaptive Imune Fuzzy Quasi-Sliding Mode Formation Tracking Control for Wheeled Mobile Robots with Obstacle Avoidance Under Incidence of Uncertainties and Disturbances: An Artificial Immune Systems Inspired Approach

This paper presents an adaptive immune fuzzy quasi-sliding mode kinematic control integrated with a PD dynamic control for the trajectory tracking and the leader-follower formation control by nonholonomic differential-drive wheeled mobile robots under incidence of uncertainties and disturbances. An immune regulation mechanism bio-inspired approach with reaction effect established by novel fuzzy rules set to adjust the control effort adaptively is designed, also using a fuzzy boundary layer and introducing an adaptation law for the immune portion gain online adjustment in such a way that they can also avert parameter drift, dealing with the drawbacks of a classic first-order sliding mode control, suppressing chattering and still maintaining the robustness with no a priori knowledge of the bounds of the disturbances. An obstacle avoidance strategy with a reactive method and variable avoidance radius is also proposed. The stability analysis is performed based on the Lyapunov theory. Simulation results demonstrate the proposed control effectiveness.

Adaptive immune fuzzy quasi-sliding mode control for leader–follower formation of wheeled mobile robots under uncertainties and disturbances with obstacle avoidance

PurposeThis paper aims to propose a robust kinematic controller based on sliding mode theory designed to solve the trajectory tracking problem and also the formation control using the leader–follower strategy for nonholonomic differential-drive wheeled mobile robots with a PD dynamic controller.Design/methodology/approachTo deal with classical sliding mode control shortcomings, such as the chattering and the requirement of a priori knowledge of the limits of the effects of disturbances, an immune regulation mechanism-inspired approach is proposed to adjust the control effort magnitude adaptively. A simple fuzzy boundary layer method and an adaptation law for the immune portion gain online adjustment are also considered. An obstacle avoidance reactive strategy is proposed for the leader robot, given the importance of the leader in the formation control structure.FindingsTo verify the adaptability of the controller, obstacles are distributed along the reference trajectory, and the simulation and experimental results show the effectiveness of the proposed controller, which was capable of generating control signals avoiding chattering, compensating for disturbances and avoiding the obstacles.Originality/valueThe proposed design stands out for the ability to adapt in a case involving obstacle avoidance, trajectory tracking and leader–follower formation control by nonholonomic robots under the incidence of uncertainties and disturbances and also considering that the immune-based control provided chattering mitigation by adjusting the magnitude of the control effort, with adaptability improved by a simple integral-type adaptive law derived by Lyapunov stability analysis.

A U-shaped CNN with type-2 fuzzy pooling layer and dynamical feature extraction for colorectal polyp applications

A U-shaped CNN with type-2 fuzzy pooling layer and dynamical feature extraction for colorectal polyp applications

Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation.

Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed methods to automatically segment airways from computerized tomography (CT) images. However, some small-sized airway branches (e.g., bronchus and terminal bronchioles) significantly aggravate the difficulty of automatic segmentation by machine learning models. In particular, the variance of voxel values and the severe data imbalance in airway branches make the computational module prone to discontinuous and false-negative predictions, especially for cohorts with different lung diseases. The attention mechanism has shown the capacity to segment complex structures, while fuzzy logic can reduce the uncertainty in feature representations. Therefore, the integration of deep attention networks and fuzzy theory, given by the fuzzy attention layer, should be an escalated solution for better generalization and robustness. This article presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network (FANN) and a comprehensive loss function to enhance the spatial continuity of airway segmentation. The deep fuzzy set is formulated by a set of voxels in the feature map and a learnable Gaussian membership function. Different from the existing attention mechanism, the proposed channel-specific fuzzy attention addresses the issue of heterogeneous features in different channels. Furthermore, a novel evaluation metric is proposed to assess both the continuity and completeness of airway structures. The efficiency, generalization, and robustness of the proposed method have been proved by training on normal lung disease while testing on datasets of lung cancer, COVID-19, and pulmonary fibrosis.

An information entropy-based fuzzy stochastic configuration network for robust data modeling

In data-driven modeling, the generalization performance of a neural network based on the minimum mean square error criterion may be affected when the samples contain noise. To handle uncertain data modeling problems, this study contributes a novel robust data modeling method called information entropy-based fuzzy stochastic configuration networks (IEFSCNs), which incorporates the maximum correntropy criterion (MCC) to reflect the squared error of the training sample set and uses the quantized minimum error entropy (QMEE) criterion to mine the distribution structure of the modeling data and suppress outlier data. Fuzzy systems are designed to generate nodes in the fuzzy transformation layer. A nonlinear enhancement layer is established as a hidden layer to enhance the representational capacity of the target system. The fuzzy transformation and nonlinear enhancement layers are fully connected to determine the outputs of the IEFSCNs. In addition, an incremental parameter learning algorithm is developed based on a supervision mechanism for optimizing the cost function, and its convergence property is demonstrated. Owing to the superiorities of the cost function by synthesizing MCC and QMEE, IEFSCNs can improve the modeling accuracy and reduce noise suppression. The performances are evaluated using various regression and classification tasks. Compared with existing methods, including SCNs, RVFL, F-SCNs, RSC-KDE, RSC-MCC, and SM-RSC, the proposed method demonstrates an effective robust modeling performance by reducing network structure redundancy while enhancing its ability to suppress noisy data.

Fuzzy convolutional neural network model applied to classification problems

This paper presents an approach incorporating fuzzy logic techniques inside a convolutional neural network to manage uncertainty present in the multiple data sources that the model handles when training. The implementation considers the use of information and filters in the fuzzy spectrum, as well as the creation of a new layer to replace the traditional convolution layer with a fuzzy convolutional layer. The aim is to design artificial intelligence algorithms that combine the potential of deep convolutional neural networks and fuzzy logic to create robust systems that allow modeling the uncertainty present in the sources of data and that are applied to classification problems. The fuzzification process is developed using three membership functions, including the Triangular, Gaussian, and S functions. The work was tested in databases oriented to traffic signs, due to the complexity of the different circumstances and factors in which a traffic sign can be found.

Solving NP-Hard Challenges in Logistics and Transportation under General Uncertainty Scenarios Using Fuzzy Simheuristics

In the field of logistics and transportation (L&T), this paper reviews the utilization of simheuristic algorithms to address NP-hard optimization problems under stochastic uncertainty. Then, the paper explores an extension of the simheuristics concept by introducing a fuzzy layer to tackle complex optimization problems involving both stochastic and fuzzy uncertainties. The hybrid approach combines simulation, metaheuristics, and fuzzy logic, offering a feasible methodology to solve large-scale NP-hard problems under general uncertainty scenarios. These scenarios are commonly encountered in L&T optimization challenges, such as the vehicle routing problem or the team orienteering problem, among many others. The proposed methodology allows for modeling various problem components—including travel times, service times, customers’ demands, or the duration of electric batteries—as deterministic, stochastic, or fuzzy items. A cross-problem analysis of several computational experiments is conducted to validate the effectiveness of the fuzzy simheuristic methodology. Being a flexible methodology that allows us to tackle NP-hard challenges under general uncertainty scenarios, fuzzy simheuristics can also be applied in fields other than L&T.

Enhancement of medical images diagnosis using fuzzy convolutional neural network

Brain diseases are primarily brought on by abnormal brain cell growth, which can harm the structure of the brain and eventually result in malignant brain cancer. Major challenges exist when using a computer aided diagnosis (CAD) system for an early diagnosis that enables decisive treatment, particularly when it comes to the accurate detection of various diseases in the pictures for magnetic resonance imaging (MRI). In this study, the fuzzy convolutional neural networks (FCNN) were proposed for accurate diagnosis of brain tumors (glioma, meningioma, pituitary and non-tumor) which is implemented using Keras and TensorFlow. This approach follows three steps, training, testing, and evaluation. In training process, it builds a smart model and the structure consists of seven blocks (convolution, rectified linear unit (ReLU), batch normalization, and max pooling) then use flatten, fuzzy inferences layer, and dense layer with dropout. An international dataset with 7,022 brain tumor MRI images, was tested. The evaluation model attained a high performance with training accuracy of 99.84% and validation accuracy is 98.63% with low complexity and time is 58 s per epoch. The suggested approach performs better than the other known algorithms and may be quickly and accurately used for medical picture diagnosis.

FUKG: answering flexible queries over knowledge graphs

PurposeThere is an increasing interest in the use of knowledge graphs to represent real-world knowledge and a common need to manage imprecise knowledge in many real-world applications. This paper aims to study approaches to solve flexible queries over knowledge graphs.Design/methodology/approachBy introducing fuzzy logic in the query answering process, the authors are able to obtain a novel algorithm to solve flexible queries over knowledge graphs. This approach is implemented in the FUzzy Knowledge Graphs system, a software tool with an intuitive user-graphical interface.FindingsThis approach makes it possible to reuse semantic web standards (RDF, SPARQL and OWL 2) and builds a fuzzy layer on top of them. The application to a use case shows that the system can aggregate information in different ways by selecting different fusion operators and adapting to different user needs.Originality/valueThis approach is more general than similar previous works in the literature and provides a specific way to represent the flexible restrictions (using fuzzy OWL 2 datatypes).

Investigation of the Influence of Nanoscale Porosity in the Interfacial Layers on the Mechanical Properties of Helium Plasma-Exposed Tungsten

Porous layers emergent from the bulk tungsten (W) interface subsequent to helium (He) plasma exposure, under conditions relevant to thermonuclear experiments, were found to significantly modulate the structural attributes of the W─as deduced through nanoscale indentation measurements. Plastic deformation of nanofibers constituting the fuzzy surface layer, along with an increased yield strength with layer depth, was quantified. A power-law relationship between the indentation modulus (Eind) and the porosity of the fuzzy W was indicated. The surface roughness of the bulk W increases with temperature prior to fuzz formation, determined by AFM. A decrease in the Eind of the bulk W underneath the fuzzy layer with increasing sample temperature when exposed to plasma, in agreement with molecular-dynamics computations, was observed.

The many facets of micropetrosis - Magnesium whitlockite deposition in bisphosphonate-exposed human alveolar bone with osteolytic metastasis.

The lacuno-canalicular space of apoptotic osteocytes eventually becomes mineralised in vivo. This condition is known as micropetrosis and is a fundamental characteristic of ageing bone. Increased prevalence of such hypermineralised osteocyte lacunae is viewed as a structural marker of impaired bone function - both mechanical and biological. Within the lacuno-canalicular space, mineralised apoptotic debris typically occurs as micrometre-sized, spherical nodules of magnesium-rich, carbonated apatite. Moreover, characteristically facetted, rhomboidal nodules of magnesium whitlockite [Mg-whitlockite; Ca18Mg2(HPO4)2(PO4)12] have been reported in human alveolar bone exposed to the bisphosphonate alendronate. This work provides supporting evidence for Mg-whitlockite formation in the alveolar bone of a 70-year-old male exposed to the bisphosphonate zoledronic acid to suppress osteolytic changes in skeletal metastasis. Backscattered electron scanning electron microscopy (BSE-SEM) revealed spherical and rhomboidal nodules within the lacuno-canalicular space. A variant of spherical nodules exhibited a fuzzy surface layer comprising radially extending acicular crystallites. The rhomboidal nodules ranged between ∼200nm to ∼2.4µm across the widest dimension (652±331nm). Micro-Raman spectroscopy and energy dispersive X-ray spectroscopy confirmed that rhomboidal nodules are compositionally distinct from spherical nodules, exhibiting higher Mg content and lower Ca/P ratio. Formation of Mg-whitlockite within osteocyte lacunae is multifactorial in nature and suggests altered bone biomineralisation. Nevertheless, the underlying mechanism(s) and sequence of events remain poorly understood and warrant further investigation. The possibility to discriminate between carbonated apatite and Mg-whitlockite nodules within osteocyte lacunae, based on particle morphology, attests to the diagnostic potential of BSE-SEM with or without additional analyses of material composition.

The Analysis of Dual Axis Solar Tracking System Controllers Based on Adaptive Neural Fuzzy Inference System (ANFIS)

Artificial intelligence is commonly used in Photovoltaic (PV) control systems. Adaptive Neural Fuzzy Inference System (ANFIS) is one of the intelligent strategies that can be employed in the system controller. ANFIS technique shows high accuracy as it involved several processes which are the Fuzzy layer, Fuzzy Rule layer, Normalization layer, and Output Membership layer. The main objective of the proposed work is to model the dual-axis solar tracker using MATLAB software by utilizing the ANFIS technique, hence improving the performance of the solar system. The data used for training and testing are elevation angle and azimuth angle. 80% of the data is used for training and another 20% for testing in order to predict the solar radiation toward PV panels. A different set of input membership functions (MFs) is used in the system, which are Five MFs, Ten MFs, and Fifteen MFs. These MF are simulated to produce the best prediction of solar radiation. The results showaverage error gained for both training and testing data and minimum error indicates the accuracy of the predicted angle of dual axis solar tracker. In the finding, overall results show a good correlation between the actual and prediction value with 15 input MFs as it produced the lowest error value.

Design of an efficient IoT based irrigation platform via fuzzy bioinspired multisensory analysis of on-field parameter sets

Design of smart irrigation systems is a transdisciplinary work involving selecting optimal field-specific sensing devices, data capturing from these devices, their pre-processing, feature analysis, classification, IoT (Internet of Things) based actuation, and feedback operations. A wide variety of smart irrigation models, including IBM ThingSpeak, Tracxn, Farm Connect, Hydrawise, etc. have been proposed by researchers, and each of them have its own operational & deployment-specific characteristics. These models are either highly complex or have slower response time & low efficiency when applied to real-time irrigation scenarios. Moreover, these models have a higher cost, which limits their scalability levels. To overcome these issues, this text discusses design of a novel efficient IoT based irrigation platform via fuzzy bioinspired multisensory analysis of on-field parameter sets. The proposed platform uses low-cost components for sensing moisture levels, temperature levels, rain probability, NPK (Nitrogen, Phosphorous, and Potassium) levels, and soil types. These sensed values are processed via a Grey Wolf Optimizer (GWO) to identify optimum sensor types, and then a fuzzy decision layer is used for irrigation. This layer assists in the identification of efficient water flow for different crop types. The resulting growth of plants is fed-back into the model, and a Genetic Algorithm (GA) is applied to tune the fuzzy rules for better water-flow under multiple types of crops. Due to the integration of GWO with Fuzzy Logic controller, the proposed model improved yield efficiency by 8.5%, reduced computational delay by 4.9%, and reduced deployment cost compared to standard smart irrigation models. Due to these advantages, the proposed model is capable of deployment for a wide variety of real-time smart irrigation use cases.

Sarcasm Detection over Social Media Platforms Using Hybrid Ensemble Model with Fuzzy Logic

A figurative language expression known as sarcasm implies the complete contrast of what is being stated with what is meant, with the latter usually being rather or extremely offensive, meant to offend or humiliate someone. In routine conversations on social media websites, sarcasm is frequently utilized. Sentiment analysis procedures are prone to errors because sarcasm can change a statement’s meaning. Analytic accuracy apprehension has increased as automatic social networking analysis tools have grown. According to preliminary studies, the accuracy of computerized sentiment analysis has been dramatically decreased by sarcastic remarks alone. Sarcastic expressions also affect automatic false news identification and cause false positives. Because sarcastic comments are inherently ambiguous, identifying sarcasm may be difficult. Different individual NLP strategies have been proposed in the past. However, each methodology has text contexts and vicinity restrictions. The methods are unable to manage various kinds of content. This study suggests a unique ensemble approach based on text embedding that includes fuzzy evolutionary logic at the top layer. This approach involves applying fuzzy logic to ensemble embeddings from the Word2Vec, GloVe, and BERT models before making the final classification. The three models’ weights assigned to the probability are used to categorize objects using the fuzzy layer. The suggested model was validated on the following social media datasets: the Headlines dataset, the “Self-Annotated Reddit Corpus” (SARC), and the Twitter app dataset. Accuracies of 90.81%, 85.38%, and 86.80%, respectively, were achieved. The accuracy metrics were more accurate than those of earlier state-of-the-art models.

Rule-based fuzzy neural networks realized with the aid of linear function Prototype-driven fuzzy clustering and layer Reconstruction-based network design strategy

In this study, we introduce novel fuzzy neural networks designed with the aid of linear function prototype-driven fuzzy clustering (LFPFC) and layer reconstruction-based network design strategy to deal with the regression problem. The LFPFC constitutes a new clustering technique inspired by the fuzzy c-regression model (FCRM) clustering unlike fuzzy c-means (FCM) clustering LFPFC represents the prototypes of clusters as linear functions, and this can lead to more reliable data analysis of complex regression problems. We propose two types of LFPFC such as an estimated output-based LFPFC and a distance-based LFPFC. The estimated output-based LFPFC uses the output estimated on a basis of the simple model instead of the target output to calculate the centroid of LFPFC. A centroid of distance-based LFPFC is computed through the Euclidean distance between input data and the centroid of the cluster. By using two kinds of LFPFC approaches, we propose three different types of fuzzy neural networks: i) the fuzzy neural networks through layer reconstruction-based network design strategy consists of two models. The first model serves as an estimate of the desired output and the estimated output is used in the LFPFC of the second model. ii) In the fuzzy neural networks applied to the basic architecture of distance-based LFPFC, the hidden layer using the membership function changes to basic distance-based LFPFC, and the partition matrix obtained from LFPFC is used as the output of the hidden layer. iii) in the fuzzy neural network with the advanced architecture of distance-based LFPFC, an additional auxiliary layer is considered between the hidden and output layers to estimate the membership function of output space through LFPFC. In the experiments, we evaluate the performance index of the proposed models using publicly available machine learning datasets. The superiority of the proposed fuzzy neural networks designed by using LFPFC is demonstrated through the comparative analysis with the diverse regression models offered in the Weka data mining software. By conducting the Friedman test we show that the proposed model exhibits visible competitiveness from the viewpoint of performance. In addition, a real-world Portland cement dataset is dealt with to demonstrate the superiority of the models designed with the aid of LFPFC and reinforced layer reconstruction-based network design strategy.

Fault prediction using fuzzy convolution neural network on IoT environment with heterogeneous sensing data fusion

Using multi-source sensing data based on the Internet of things and fusion in conjunction with fuzzy convolutional neural networks to classify and predict mechanical failures has emerged as a very pertinent area of research. This study presents a fuzzy convolutional neural network-based technique to extract and diagnose the defect indicators utilizing the bearing dataset from Case Western Reserve University (CWRU). In addition, the proposed model's effectiveness is evaluated with the help of a model based on a direct convolution neural network. The strategy based on Fuzzification yielded superior results when applied to a fully linked layer for classification. When applied to the bearing dataset, the proposed approach produces an average classification accuracy of 99.87% using the fuzzy layer. The proposed method is evaluated compared to other methods based on machine learning and deep learning. The accuracy achieved using the proposed method is superior to that achieved using the existing approaches in every imaginable working environment. The proposed approach can be used to diagnose mechanical motor faults and provide earlier recommendations.

FuzzyGAN: Fuzzy generative adversarial networks for regression tasks

Generative Adversarial Networks (GANs) are well-known tools for data generation and semi-supervised classification. GANs, with less labeled data, outperform Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) in classification tasks. The success of GANs in classification tasks motivates the development of GAN-based techniques for semi-supervised regression tasks. However, developing GANs for regression introduces two major challenges: (1) inherent instability in the GAN formulation and (2) performing regression and achieving stability simultaneously. This paper introduces techniques that show improvement in the GANs’ regression capability. We bake a differentiable fuzzy logic system at multiple locations in a GAN. The fuzzy logic takes the output of either the generator or the discriminator to predict the output, y, and evaluate the generator’s performance. We outline the results of applying the fuzzy logic system across multiple GANs and summarize each approach’s efficacy. This paper shows that adding a fuzzy logic layer can enhance GAN’s ability to perform regression; the most desirable injection location is problem-specific, and we show this through experiments over various datasets. Besides, we demonstrate empirically that the fuzzy-infused GANs are competitive with the DNNs.

LDRepFM: A Real-Time End-to-End Visible and Infrared Image Fusion Model Based on Layer Decomposition and Re-Parameterization

Visible (VIS) and infrared (IR) image fusion (VIF) is a technique used to synthesize the fused image of high visual perception. Existing fusion methods typically work by discovering the commons underlying the two modalities and fusing them in the common space. However, these methods often ignore the modality differences such as fuzzy details in the IR image and their well-designed architectures also lead to slow fusion speed. To address these issues, we propose a real-time end-to-end VIF model based on layer decomposition and re-parameterization (LDRepFM). This model is composed of a Layer Decomposition Guidance Network (LDGNet) and a Re-parameterization Fusion Network (RepFNet). Firstly, the LDGNet is used to alleviate the visual quality degradation of the fused image by decomposing the IR image into structural layer and fuzzy layer. Secondly, in order to achieve a favorable trade-off between the fusion speed and evaluation metrics, the RepFNet is utilized to decouple the training-time multi-branch and inference-time plain architecture. Thirdly, the structural layer that has been decomposed by LDGNet is utilized in constructing the guidance fusion loss, which is aimed at optimizing RepFNet. Finally, experiments conducted on the publicly available TNO, RoadScene, M3FD, and RegDB datasets demonstrate the performance of the proposed method to be comparable to the state-of-the-art in terms of both visual effect and quantitative metrics. The code is publically available at https://github.com/luming1314/LDRepFM.

A novel fuzzy hierarchical fusion attention convolution neural network for medical image super-resolution reconstruction

The clarity of medical images is crucial for doctors to identify and diagnose different diseases. High-resolution images have more detailed information and clearer content than low-resolution images. It is well known that medical images can frequently have some blurred object boundaries, and that traditional deep learning models cannot adequately describe the uncertainty of these blurred boundaries. This paper proposes a new fuzzy metric to characterize the uncertainty of pixels and designs a fuzzy hierarchical fusion attention neural network based on multiscale guided learning. Specifically, a fuzzy neural information-processing block is proposed, which converts an input image into a fuzzy domain using fuzzy membership functions. The uncertainty of the pixels is processed using the proposed fuzzy rules, and then the output of the fuzzy rule layer is fused with the result of the convolution in the neural network. Simultaneously, a multiscale guided-learning dense residual block and pyramidal hierarchical attention module are designed to extract more effective hierarchical image information. Finally, a recurrent memory module with a residual structure is used to process the output features of the hierarchical attention modules. A recursive sub-pixel reconstruction module is used at the tail of the network to reconstruct the images. Compared with existing super-resolution methods using the public COVID-CT dataset, the proposed method demonstrated superior performance in high-resolution medical image reconstruction and reduced the number of parameters and analysis time of the models.