Study compares GFMM neural networks for pattern classification.
problem Pattern classification problems.
method Two algorithms (incremental and agglomerative learning) to train GFMM neural networks.
result GFMM neural networks show strong and weak points on benchmark datasets.
Improved online learning for fuzzy min-max neural networks.
problem Classification performance issues due to expansion and contraction steps.
method Proposes an improved online learning algorithm without contraction for overlapping hyperboxes.
result Significant improvement in classification accuracy and stability.
Paper investigates differentiable fuzzy implications and their suitability for learning.
problem Analyzing differentiable fuzzy implications and their suitability for learning.
method Investigates the properties of fuzzy implications in a differentiable setting and introduces a new family of fuzzy implications.
result Various fuzzy implications are unsuitable for differentiable learning, and a new family of fuzzy implications is introduced.
Takagi-Sugeno-Kang (TSK) fuzzy systems are very useful machine learning models for regression problems. However, to our knowledge, there has not existed an efficient and effective training algorithm that ensures their generalization performance, and also enables them to deal with big data. Inspired by the connections b…
Fuzzy systems have achieved great success in numerous applications. However, there are still many challenges in designing an optimal fuzzy system, e.g., how to efficiently optimize its parameters, how to balance the trade-off between cooperations and competitions among the rules, how to overcome the curse of dimensiona…
Paper proposes a faster method for fuzzy neural networks by removing unsuitable hyperboxes.
problem Reducing training time for fuzzy neural networks.
method A novel hyperbox selection rule based on mathematical formulas to remove unsuitable hyperboxes.
result Significant decrease in training time for both online and agglomerative learning algorithms.
Paper develops a neural-fuzzy controller for GPS-intelligent buoys.
problem Optimally track dynamically positioned marine buoys with unknown parameters.
method Dynamic system modeling using neural-fuzzy networks with backstepping technique.
result The controller minimizes position errors and adjusts buoy positions accurately.
We revisit fuzzy neural network with a cornerstone notion of generalized hamming distance, which provides a novel and theoretically justified framework to re-interpret many useful neural network techniques in terms of fuzzy logic. In particular, we conjecture and empirically illustrate that, the celebrated batch normal…
New deep learning models inspired by fuzzy logic are more robust to adversarial attacks.
problem Flawed generalization in deep neural networks leading to adversarial examples.
method Inspired by fuzzy logic, new architectures combining alternative design elements.
result New models are more robust to adversarial examples and noise.
Survey on understanding neural networks for medical applications.
problem Black-box nature of deep neural networks hinders their use in critical applications.
method Comprehensive review of interpretability studies in neural networks.
result Interpretability research is crucial for the acceptance of neural networks in medical diagnosis.
A new adaptive binarization technique using fuzzy integrals improves image quality.
problem Improving image thresholding quality.
method FLAT (Fuzzy Local Adaptive Thresholding) based on fuzzy integrals.
result The proposed FLAT method produces better image quality than traditional algorithms and neural networks.
Introduces fuzzy layers to enhance deep learning performance.
problem Improving deep learning accuracy for complex tasks.
method Incorporates fuzzy methodologies into deep learning architecture.
result Preliminary results show promising performance in semantic segmentation.
Paper optimizes TSK fuzzy systems for large datasets with MBGD and novel regularization.
problem Optimizing TSK fuzzy systems for large datasets with high dimensionality.
method Proposes MBGD with UR and BN for TSK fuzzy classifiers.
result UR and BN improve classification performance on various UCI datasets.
This paper compares methods for handling mixed-attribute data in GFMM neural networks.
problem Handling datasets with mixed features in GFMM neural networks.
method Three main methods: encoding, combining with other classifiers, and specific learning algorithms.
result Encoding methods and combining with decision trees improve GFMM models' performance.
Generates music with video emotion using deep neural networks.
problem Generating music with video emotion.
method Hybrid deep neural network combining ANFIS and LSTM.
result Low mean absolute errors and similar global features in spectrograms.
Paper uses ANFIS to predict cryptocurrency prices.
problem Predicting cryptocurrency prices for seven days.
method Adaptive Network Based Fuzzy Inference System (ANFIS) with hybrid and backpropagation algorithms.
result The method can predict cryptocurrency prices in a short time.
Study enhances financial forecasting with machine learning and fuzzy MCDM.
problem Increasing financial uncertainty and market complexity.
method Integrates machine learning (XGBoost, LSTM, GNN) and intuitionistic fuzzy MCDM.
result High forecasting accuracy with low MAPE and narrow confidence intervals.
Paper proposes an online learning algorithm for a neuro-fuzzy classifier with mixed data.
problem Inability of GFMMNN learning algorithms to handle mixed-attribute data.
method Extended online learning algorithm for GFMMNN that can handle both continuous and categorical features.
result Superior and stable classification performance compared to other learning algorithms.
Neural networks optimize stopping boundaries in financial instruments.
problem Optimizing stopping boundaries in financial instruments.
method Deep neural networks and empirical risk minimization for parameterizing stopping boundaries.
result Proved existence of stopping boundary under natural assumptions.
Motivated by the practical demands for simplification of data towards being consistent with human thinking and problem solving as well as tolerance of uncertainty, information granules are becoming important entities in data processing at different levels of data abstraction. This paper proposes a method to construct c…
This study proposes methods for multi-step-ahead stock price prediction using decomposition and neural networks.
problem Inaccurate one-step-ahead forecasting limits stock market decision-making.
method Two novel methods: DCT-MFRFNN and VMD-MFRFNN.
result VMD-MFRFNN outperforms other methods in multi-step-ahead stock price prediction.
Paper analyzes consistency between neural networks at different levels.
problem Understanding knowledge consistency between neural networks.
method Generic definition and task-agnostic method to disentangle consistent knowledge.
result Knowledge consistency provides new insights and can improve network performance.
A new model for detecting overlapping communities in weighted networks.
problem Community detection in overlapping weighted networks with mixed membership and edge weights.
method Mixed membership distribution-free (MMDF) model with an efficient spectral algorithm and fuzzy weighted modularity.
result The MMDF model can estimate community memberships and evaluate community quality for weighted networks.
Measuring the similarity of two files is an important task in malware analysis, with fuzzy hash functions being a popular approach. Traditional fuzzy hash functions are data agnostic: they do not learn from a particular dataset how to determine similarity; their behavior is fixed across all datasets. In this paper, we …
The problem of learning a manifold structure on a dataset is framed in terms of a generative model, to which we use ideas behind autoencoders (namely adversarial/Wasserstein autoencoders) to fit deep neural networks. From a machine learning perspective, the resulting structure, an atlas of a manifold, may be viewed as …
Application of neural network architectures for financial prediction has been actively studied in recent years. This paper presents a comparative study that investigates and compares feed-forward neural network (FNN) and adaptive neural fuzzy inference system (ANFIS) on stock prediction using fundamental financial rati…
Explaining neural network computation in terms of probabilistic/fuzzy logical operations has attracted much attention due to its simplicity and high interpretability. Different choices of logical operators such as AND, OR and XOR give rise to another dimension for network optimization, and in this paper, we study the o…
The hybrid clustering-classification neural network is proposed. This network allows increasing a quality of information processing under the condition of overlapping classes due to the rational choice of a learning rate parameter and introducing a special procedure of fuzzy reasoning in the clustering process, which o…
The paper develops models to predict the remaining useful life of water pipes.
problem Predicting the remaining useful life of deteriorating water pipes.
method Artificial Neural Networks (ANNs) and Adaptive Neuro-fuzzy Inference System (ANFIS) applied to field data.
result Models accurately predict the reduction of remaining useful life due to pipe deterioration.
Paper simulates LR fuzzy intervals with interval-valued cores.
problem Generating random fuzzy intervals with interval-valued cores.
method Developed algorithms for simulating LR fuzzy numbers with interval-valued cores.
result Numerically efficient algorithm for simulating fuzzy values.
The artificial neural network is a popular framework in machine learning. To empower individual neurons, we recently suggested that the current type of neurons could be upgraded to 2nd order counterparts, in which the linear operation between inputs to a neuron and the associated weights is replaced with a nonlinear qu…
In this paper, we have tried to apply the concepts of fuzzy sets to Lie groups and its relative concepts. First, we define a C1 fuzzy submanifold after reviewing C1−fuzzy manifold definition. In main section, we defined the Lie group and some its relative concepts such as fuzzy transformation group,…
Unified framework for hierarchical image classification with epistemic uncertainty.
problem Overconfident predictions and lack of logical consistency in deep learning models.
method Neurosymbolic approach with epistemic deep learning, using focal set reasoning and differentiable fuzzy logic.
result Maintains accuracy on par with transformer baselines while providing more calibrated and interpretable predictions.
Fuzzy eIX method evolves classifiers for online data streams.
problem Handling time-varying classifiers in online data streams.
method Develops evolving Internal-eXternal Fuzzy granules for numerical data.
result Fuzzy eIX maintains high accuracy in dynamic scenarios.
We propose a mathematical model for the word-of-mouth communications among stock investors through social networks and explore how the changes of the investors' social networks influence the stock price dynamics and vice versa. An investor is modeled as a Gaussian fuzzy set (a fuzzy opinion) with the center and standar…
New method detects anomalies in computing centers' logs.
problem Anomaly detection in continuously changing log data for predictive maintenance.
method Evolving granular classifiers using Fuzzy-set-Based evolving Modeling and evolving Granular Neural Network.
result Classification model prioritizes maintenance based on anomaly severity.
Logifold improves ensemble machine learning by identifying fuzzy domains.
problem Improving ensemble machine learning accuracy.
method Formulating logifold structure and interpreting local charts of datasets.
result Logifold improves accuracy compared to averaging model outputs.
Fuzzy prediction sets generalize binary predictions to include elements at varying confidence levels.
problem Binary prediction sets are limited; fuzzy prediction sets offer richer guarantees.
method Generalize prediction sets to fuzzy sets, showing they are e-values with merging properties.
result Optimal e-values lead to optimal fuzzy prediction sets, including optimal conformal prediction.
Paper defines kernels for fuzzy sets, useful in machine learning.
problem Uncertainty in data with fuzzy set membership.
method Defined cross product, intersection, non-singleton, and distance-based kernels.
result Kernels improve machine learning tasks with fuzzy data.
Robust fuzzy clustering for EEG driver alertness with outlier detection.
problem Ambiguous state boundaries in multivariate time series data.
method RFCPCA, a robust fuzzy subspace-clustering method for MTS.
result RFCPCA improves clustering accuracy and characterizes uncertainty and outliers in MTS.
Develops a logifold structure for understanding datasets.
problem Understanding and classifying complex datasets.
method Local-to-global approach using measure-theoretical models.
result Improves accuracy in data classification problems.
Enhanced fuzzy system predicts chaotic time series with improved accuracy.
problem Forecasting chaotic time series with high uncertainty.
method Combines evolving fuzzy systems, participatory learning, KRLS, and type-2 fuzzy sets.
result Proposed model outperforms other methods in accuracy and complexity.
A deep convolutional fuzzy system (DCFS) on a high-dimensional input space is a multi-layer connection of many low-dimensional fuzzy systems, where the input variables to the low-dimensional fuzzy systems are selected through a moving window across the input spaces of the layers. To design the DCFS based on input-outpu…
SESSC clusters fuzzy rules for TSK classifiers, improving performance with label info.
problem Lack of supervised clustering for TSK fuzzy classifiers.
method SESSC integrates within-cluster compactness, between-cluster separation, and label information.
result SESSC initialization outperforms other clustering methods, especially for small rule numbers.
A new fuzzy clustering method using hyperbolic smoothing for large datasets.
problem Building fuzzy clusters for large data sets efficiently.
method A novel smoothing numerical approach to relax the sum-of-squares criterion, converting the problem into a differentiable optimization problem.
result The method produces better fuzzy partitions compared to traditional fuzzy C-means. In this paper we use fuzzy systems theory to convert the technical trading rules commonly used by stock practitioners into excess demand functions which are then used to drive the price dynamics. The technical trading rules are recorded in natural languages where fuzzy words and vague expressions abound. In Part I of t…
Unified probabilistic foundation for fuzzy simplicial sets in dimensionality reduction.
problem Lack of clear probabilistic interpretation in fuzzy simplicial sets.
method Introducing a probabilistic framework explaining fuzzy simplicial sets as marginals of probability measures on simplicial sets.
result Unified probabilistic theoretical foundation for fuzzy simplicial sets.
Fuzzy clustering reduces text data dimensions effectively.
problem Sparsity and high dimensionality in document-term frequency matrices.
method Fuzzy clustering applied as a new unsupervised feature transformation method.
result Fuzzy clustering outperforms PCA and SVD in reducing text data dimensions.