MODELLING EXPECTATIONS WITH GENEFER- AN ARTIFICIAL INTELLIGENCE APPROACH

Economic modelling of financial markets means to model highly complex systems in which expectations can be the dominant driving forces. Therefore it is necessary to focus on how agents form their expectations. We believe that they look for patterns, hypothesize, try, make mistakes, learn and adapt. AgentsÆ bounded rationality leads us to a rule-based approach which we model using Fuzzy Rule-Bases. E. g. if a single agent believes the exchange rate is determined by a set of possible inputs and is asked to put their relationship in words his answer will probably reveal a fuzzy nature like: "IF the inflation rate in the EURO-Zone is low and the GDP growth rate is larger than in the US THEN the EURO will rise against the USD". æLowÆ and ælargerÆ are fuzzy terms which give a gradual linguistic meaning to crisp intervalls in the respective universes of discourse. In order to learn a Fuzzy Fuzzy Rule base from examples we introduce Genetic Algorithms and Artificial Neural Networks as learning operators. These examples can either be empirical data or originate from an economic simulation model. The software GENEFER (GEnetic NEural Fuzzy ExplorER) has been developed for designing such a Fuzzy Rule Base. The design process is modular and comprises Input Identification, Fuzzification, Rule-Base Generating and Rule-Base Tuning. The two latter steps make use of genetic and neural learning algorithms for optimizing the Fuzzy Rule-Base.

An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams

Existing FNNs are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This paper proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be automatically extracted from data streams or removed if they play limited role during their lifespan. The structure of the network can be deepened on demand by stacking additional layers using a drift detection method which not only detects the covariate drift, variations of input space, but also accurately identifies the real drift, dynamic changes of both feature space and target space. DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely gClass, drives the hidden layer. It is equipped by an automatic feature selection method which controls activation and deactivation of input attributes to induce varying subsets of input features. A deep network simplification procedure is put forward using the concept of hidden layer merging to prevent uncontrollable growth of dimensionality of input space due to the nature of feature augmentation approach in building a deep network structure. DEVFNN works in the sample-wise fashion and is compatible for data stream applications. The efficacy of DEVFNN has been thoroughly evaluated using seven datasets with non-stationary properties under the prequential test-then-train protocol. It has been compared with four popular continual learning algorithms and its shallow counterpart where DEVFNN demonstrates improvement of classification accuracy. Moreover, it is also shown that the concept drift detection method is an effective tool to control the depth of network structure while the hidden layer merging scenario is capable of simplifying the network complexity of a deep network with negligible compromise of generalization performance.Comment: This paper has been published in IEEE Transactions on Fuzzy System

A new data-driven neural fuzzy system with collaborative fuzzy clustering mechanism

© 2015 Elsevier B.V. In this paper, a novel fuzzy rule transfer mechanism for self-constructing neural fuzzy inference networks is being proposed. The features of the proposed method, termed data-driven neural fuzzy system with collaborative fuzzy clustering mechanism (DDNFS-CFCM) are; (1) Fuzzy rules are generated facilely by fuzzy c-means (FCM) and then adapted by the preprocessed collaborative fuzzy clustering (PCFC) technique, and (2) Structure and parameter learning are performed simultaneously without selecting the initial parameters. The DDNFS-CFCM can be applied to deal with big data problems by the virtue of the PCFC technique, which is capable of dealing with immense datasets while preserving the privacy and security of datasets. Initially, the entire dataset is organized into two individual datasets for the PCFC procedure, where each of the dataset is clustered separately. The knowledge of prototype variables (cluster centers) and the matrix of just one halve of the dataset through collaborative technique are deployed. The DDNFS-CFCM is able to achieve consistency in the presence of collective knowledge of the PCFC and boost the system modeling process by parameter learning ability of the self-constructing neural fuzzy inference networks (SONFIN). The proposed method outperforms other existing methods for time series prediction problems

An incremental interval Type-2 neural fuzzy Classifier

© 2015 IEEE. Most real world classification problems involve a high degree of uncertainty, unsolved by a traditional type-1 fuzzy classifier. In this paper, a novel interval type-2 classifier, namely Evolving Type-2 Classifier (eT2Class), is proposed. The eT2Class features a flexible working principle built upon a fully sequential and local working principle. This learning notion allows eT2Class to automatically grow, adapt, prune, recall its knowledge from data streams in the single-pass learning fashion, while employing loosely coupled fuzzy sub-models. In addition, eT2Class introduces a generalized interval type-2 fuzzy neural network architecture, where a multivariate Gaussian function with uncertain non-diagonal covariance matrixes constructs the rule premise, while the rule consequent is crafted by a local non-linear Chebyshev polynomial. The efficacy of eT2Class is numerically validated by numerical studies with four data streams characterizing non-stationary behaviors, where eT2Class demonstrates the most encouraging learning performance in achieving a tradeoff between accuracy and complexity

A randomized neural network for data streams

© 2017 IEEE. Randomized neural network (RNN) is a highly feasible solution in the era of big data because it offers a simple and fast working principle in processing dynamic and evolving data streams. This paper proposes a novel RNN, namely recurrent type-2 random vector functional link network (RT2McRVFLN), which provides a highly scalable solution for data streams in a strictly online and integrated framework. It is built upon the psychologically inspired concept of metacognitive learning, which covers three basic components of human learning: what-to-learn, how-to-learn, and when-to-learn. The what-to-learn selects important samples on the fly with the use of online active learning scenario, which renders our algorithm an online semi-supervised algorithm. The how-to-learn process combines an open structure of evolving concept and a randomized learning algorithm of random vector functional link network (RVFLN). The efficacy of the RT2McRVFLN has been numerically validated through two real-world case studies and comparisons with its counterparts, which arrive at a conclusive finding that our algorithm delivers a tradeoff between accuracy and simplicity

Multiobjective programming for type-2 hierarchical fuzzy inference trees

This paper proposes a design of hierarchical fuzzy inference tree (HFIT). An HFIT produces an optimum tree-like structure. Specifically, a natural hierarchical structure that accommodates simplicity by combining several low-dimensional fuzzy inference systems (FISs). Such a natural hierarchical structure provides a high degree of approximation accuracy. The construction of HFIT takes place in two phases. Firstly, a nondominated sorting based multiobjective genetic programming (MOGP) is applied to obtain a simple tree structure (low model’s complexity) with a high accuracy. Secondly, the differential evolution algorithm is applied to optimize the obtained tree’s parameters. In the obtained tree, each node has a different input’s combination, where the evolutionary process governs the input’s combination. Hence, HFIT nodes are heterogeneous in nature, which leads to a high diversity among the rules generated by the HFIT. Additionally, the HFIT provides an automatic feature selection because it uses MOGP for the tree’s structural optimization that accept inputs only relevant to the knowledge contained in data. The HFIT was studied in the context of both type-1 and type-2 FISs, and its performance was evaluated through six application problems. Moreover, the proposed multiobjective HFIT was compared both theoretically and empirically with recently proposed FISs methods from the literature, such as McIT2FIS, TSCIT2FNN, SIT2FNN, RIT2FNS-WB, eT2FIS, MRIT2NFS, IT2FNN-SVR, etc. From the obtained results, it was found that the HFIT provided less complex and highly accurate models compared to the models produced by most of the other methods. Hence, the proposed HFIT is an efficient and competitive alternative to the other FISs for function approximation and feature selectio

Deep Stacked Stochastic Configuration Networks for Lifelong Learning of Non-Stationary Data Streams

The concept of SCN offers a fast framework with universal approximation guarantee for lifelong learning of non-stationary data streams. Its adaptive scope selection property enables for proper random generation of hidden unit parameters advancing conventional randomized approaches constrained with a fixed scope of random parameters. This paper proposes deep stacked stochastic configuration network (DSSCN) for continual learning of non-stationary data streams which contributes two major aspects: 1) DSSCN features a self-constructing methodology of deep stacked network structure where hidden unit and hidden layer are extracted automatically from continuously generated data streams; 2) the concept of SCN is developed to randomly assign inverse covariance matrix of multivariate Gaussian function in the hidden node addition step bypassing its computationally prohibitive tuning phase. Numerical evaluation and comparison with prominent data stream algorithms under two procedures: periodic hold-out and prequential test-then-train processes demonstrate the advantage of proposed methodology.Comment: This paper has been published in Information Science