Fuzzy Cognitive Maps Learning Algorithms Research Articles

Pseudoinverse learning of Fuzzy Cognitive Maps for multivariate time series forecasting

Forecasting multivariate time series is an important problem considered in many real-world scenarios. To deal with that problem, several forecasting models have already been proposed, where Fuzzy Cognitive Maps (FCMs) are proved to be a suitable alternative. The key limitation of the existing FCM-based forecasting models is the lack of time-efficient learning algorithms. In this paper, we plug that gap by proposing a new FCM learning algorithm which is based on Moore–Penrose inverse. Moreover, we propose an innovative approach that equips FCM with long-term, multistep prediction capabilities. A huge advantage of our method is the lack of parameters which in the case of competitive approaches require laborious adjustment or tuning. The other added value of our method is the reduction of the processing time required to train FCM. The performed experiments revealed that FCM trained using our method outperforms the best FCM-based forecasting model reported in the literature.

A Stable, Unified Density Controlled Memetic Algorithm for Gene Regulatory Network Reconstruction Based on Sparse Fuzzy Cognitive Maps

Gene regulatory networks (GRNs) denote the interrelation among genes in the genomic level. GRNs have a sparse network structures, and as a simulation of GRNs, the density of The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge is less than 5%. So using sparse models to represent GRNs is a meaningful task. Fuzzy cognitive maps (FCMs) have been used to reconstruct GRNs. However, the networks learned by automated derivate-free methods is much denser than those in practical applications. Moreover, the performance of current sparse FCM learning algorithms is worse than what we expect. Therefore, proposing a fast, simple and sparse FCM learning algorithm is a realistic demand. Here, we propose a new unified algorithm: Density Controlled Memetic Algorithm (DC-MA) for learning sparse FCMs. As a simple and good-performance algorithm, memetic algorithm (MA) is chosen as the framework of DC-MA. In DC-MA, a new crossover operator and a mutation operator are designed to optimize the target, control the density and ensure the stability; the local search is used to improve the accuracy and a special self-learning operator is proposed to adjust density. To test the effectiveness of our algorithm, DC-MA is performed on synthetic data with varying sizes and densities. The results show that DC-MA obtains good performance in learning sparse FCMs from time series. On the benchmark datasets DREAM3, DREAM4 and large-scale GRN reconstruction DREAM5 dataset, DC-MA shows high accuracy. The good performance in learning sparse FCMs shows the effectiveness of DC-MA, and the simplicity and scalability of the framework ensure that DC-MA can be adapted to a wide range of needs.

Learning Fuzzy Cognitive Maps with modified asexual reproduction optimisation algorithm

This paper present a comparison between Fuzzy Cognitive Map (FCM) learning approaches and algorithms. FCMs are fuzzy digraphs with weights and feedback loops, consisting of nodes interconnected through directed arcs mostly used for knowledge representation and system modelling. One of the most important characteristics of FCMs is their learning capabilities. FCMs are normally constructed through experts’ opinions, thus they maybe subjective. Learning algorithms are introduced to overcome this inconvenient. One of the main problem of the new proposed algorithms is their validation. Using theoretical and experimental analysis, this research aims to (1) compare FCM learning algorithms proposed in the literature, (2) provide a validation tool for new FCM learning algorithms (3) present a new algorithm called Asexual Reproduction Optimisation (ARO) with one of its extensions – Modified ARO (MARO) – as a novel FCM learning algorithm to use the validation tool proposed. According to the findings from the literature, it seems that among FCM learning approaches, population-based algorithms perform better compared to other algorithms. Also, the testing was done in five benchmark datasets and a synthetic dataset with different node sizes using two criteria of in-sample and out-of-sample errors. The results show that MARO outperforms other algorithms in both error functions in terms accuracy and robustness.

A New Fuzzy Cognitive Map Learning Algorithm for Speech Emotion Recognition

Selecting an appropriate recognition method is crucial in speech emotion recognition applications. However, the current methods do not consider the relationship between emotions. Thus, in this study, a speech emotion recognition system based on the fuzzy cognitive map (FCM) approach is constructed. Moreover, a new FCM learning algorithm for speech emotion recognition is proposed. This algorithm includes the use of the pleasure-arousal-dominance emotion scale to calculate the weights between emotions and certain mathematical derivations to determine the network structure. The proposed algorithm can handle a large number of concepts, whereas a typical FCM can handle only relatively simple networks (maps). Different acoustic features, including fundamental speech features and a new spectral feature, are extracted to evaluate the performance of the proposed method. Three experiments are conducted in this paper, namely, single feature experiment, feature combination experiment, and comparison between the proposed algorithm and typical networks. All experiments are performed on TYUT2.0 and EMO-DB databases. Results of the feature combination experiments show that the recognition rates of the combination features are 10%–20% better than those of single features. The proposed FCM learning algorithm generates 5%–20% performance improvement compared with traditional classification networks.

Inferring causal networks using fuzzy cognitive maps and evolutionary algorithms with application to gene regulatory network reconstruction

Fuzzy cognitive maps have been widely used as abstract models for complex networks. Traditional ways to construct fuzzy cognitive maps rely on domain knowledge. In this paper, we propose to use fuzzy cognitive map learning algorithms to discover domain knowledge in the form of causal networks from data. More specifically, we propose to infer gene regulatory networks from gene expression data. Furthermore, a new efficient fuzzy cognitive map learning algorithm based on a decomposed genetic algorithm is developed to learn large scale networks. In the proposed algorithm, the simulation error is used as the objective function, while the model error is expected to be minimized. Experiments are performed to explore the feasibility of this approach. The high accuracy of the generated models and the approximate correlation between simulation errors and model errors suggest that it is possible to discover causal networks using fuzzy cognitive map learning. We also compared the proposed algorithm with ant colony optimization, differential evolution, and particle swarm optimization in a decomposed framework. Comparison results reveal the advantage of the decomposed genetic algorithm on datasets with small data volumes, large network scales, or the presence of noise.

Learning Fuzzy Cognitive Maps using Imperialist Competitive Algorithm

In this paper, a new automated Fuzzy Cognitive Maps (FCMs) learning algorithm is developed to generate FCMs from historical data. Automated FCM learning algorithms are used to model and analyze systems which are very complex and cannot be handled by experts' knowledge. The algorithm developed in this paper is based on the Imperialist Competitive Algorithm for global optimization and is called the Imperialist Competitive Learning Algorithm (ICLA). The ICLA divides the search space into several sections. It extracts the best knowledge from each section and follows a procedure to avoid local optima alongside rapid learning. Experiments have been conducted to compare the ICLA with other well-known FCM learning algorithms. The results show that in most cases, the ICLA performs better for learning FCMs in terms of solution accuracy and execution time. The testing results show clearly that the ICLA is a robust, fast and accurate FCM learning algorithm.

Yield prediction in apples using Fuzzy Cognitive Map learning approach

This work investigates the yield modeling and prediction process in apples (cv. Red Chief) using the dynamic influence graph of Fuzzy Cognitive Maps (FCMs). FCMs are ideal causal cognition tools for modeling and simulating dynamic systems. They gained momentum due to their simplicity, flexibility to model design, adaptability to different situations, and easiness of use. In general, they model the behavior of a complex system, have inference capabilities and can be used to predict new knowledge. In this work, a data driven non-linear FCM learning approach was chosen to categorize yield in apples, where very few decision making techniques were investigated. Through the proposed methodology, FCMs were designed and developed to represent experts’ knowledge for yield prediction and crop management. The developed FCM model consists of nodes linked by directed edges, where the nodes represent the main soil factors affecting yield, [such as soil texture (clay and sand content), soil electrical conductivity (EC), potassium (K), phosphorus (P), organic matter (OM), calcium (Ca) and zinc (Zn) contents], and the directed edges show the cause-effect (weighted) relationships between the soil properties and yield. The main purpose of this study was to classify apple yield using an efficient FCM learning algorithm, the non-linear Hebbian learning, and to compare it with the conventional FCM tool and benchmark machine learning algorithms. All algorithms have been implemented in the same data set of 56 cases measured in 2005 in an apple orchard located in central Greece. The analysis showed the superiority of the FCM learning approach in yield prediction.